Anxiety symptoms and their association with
HbA1c levels in children and adolescents with type
1 diabetes mellitus: a systematic review
Kirsten M. Ellens
782800
Bachelor thesis
August 2012
Supervisor: J. Van Son, MSc
Department of Medical Psychology
Tilburg University
Abstract
OBJECTIVE – Psychosocial problems are a common phenomenon among youths with diabetes
mellitus and can have a detrimental effect on disease management. This study systematically
reviewed the evidence focusing on the association between anxiety symptoms and metabolic
control in children and adolescents with type 1 diabetes mellitus (T1DM), with special attention
paid to the exacerbating and protective aspects of this association, gender and age differences.
METHOD – Digital databases (PubMed, PsychINFO, PsychARTICLES, PBSC, Cochrane Library)
were searched for articles that investigated the relationship between diabetes, anxiety and
HbA1c levels. An adjusted version of the Downs & Black (1998) checklist was used to assess the
quality of the found studies.
RESULTS – Twenty studies were included, of which seventeen had a quality score of 62.5 % or
higher. Direct exacerbating relations between anxiety and glycaemic control were found in
eleven studies. Only two studies found a significant protective relationship between anxiety and
HbA1c. Girls with T1DM consistently reported to be more anxious than boys with T1DM, but
most studies did not investigate the effect of gender in the anxiety-HbA1c connection. Five
studies clearly included younger participants, and found that younger children exhibited more
fear and better glycaemic control than older children.
CONCLUSION – There seems to be an association between anxiety symptoms and HbA1c levels in
children and adolescents with T1DM. However, this evidence is still not conclusive. Different
anxiety assessment methods were used, leading to variation in the types of anxiety assessed.
Also, wide age ranges were not uncommon in study samples, leading to potential bias. Future
research should take these limitations and possible third factors into consideration to further
investigate the relationship between anxiety and glycaemic control and its causal direction.
Key words: diabetes mellitus type 1, children, adolescents, HbA1c, anxiety
Introduction
Diabetes mellitus type 1 (T1DM) is one of the most common childhood diseases globally. It is
estimated that around 78,000 children under the age of 15 years develop T1DM annually
worldwide [1]. In most Western countries, over 90% of child and adolescent diabetes is
classified as type 1 diabetes. Less than half of these cases are diagnosed before the age of 15
years. The other 10% accounts for childhood cases of type 2 diabetes (T2DM). An increasing
trend is seen in the incidence rate of childhood T1DM in the last twenty years, with an average
increase of 3-4% per year [2]. However, the incidence of T1DM varies considerably across
nations and ethnic populations, with higher incidence rates found in Europe, the Middle East and
North America, and remarkably low incidence rates found in Asia: China 0.1 per 100,000 and
Japan 2.4 per 100,000. To compare, the incidence rate found in Finland is 57.6 per 100,000 [2,3].
Because of the nature of T1DM, diabetic youths have to deal with various stresses. This includes
the burdensome self-care regimen that lets children with T1DM assume levels of self-care
responsibilities unsuitable for their developmental level, physical limitations, the threat of
future medical complications and the need for more parental supervision. All of this leads to less
autonomy, less spontaneity, regular frustrations and feelings of inadequacy. Not surprisingly,
epidemiological studies indicate that youths with T1DM are more likely to have psychosocial
difficulties. Although most research has focused on adult samples, there is growing evidence that
diabetic youths have higher rates of psychiatric disorders than their non-diabetic peers [4-6].
Mood disorders are most common amongst diabetic youth, but anxiety, conduct and eating
disorders are also highly prevalent [5,7]. Although these studies suggest anxiety rates are lower
than depression rates, the samples used mainly consisted of restricted age groups of diabetic
youths. Given the evidence that psychological aspects can have a negative effect on glycaemic
control and self-care behaviour [8-10], this is worrying for the health outcomes of diabetic
youths with psychological difficulties.
What is diabetes type 1?
T1DM is a chronic condition that is characterized by a high level of glucose (hyperglycaemia)
and a low level of insulin in the serum (hypoinsulinism). Symptoms typical of hypoinsulinism
and hyperglycaemia include a lack of energy, weight loss, polyuria (excessive urination),
polydipsia (excessive thirst), glycosuria (excretion of glucose in the urine), ketonuria (excretion
of ketone bodies in the urine), blurring of vision and in some cases polyphagia (excessive
hunger) [3,11]. Criteria for the diagnosis are fulfilled when 1) diabetic symptoms are present
plus plasma glucose is >11.1 mmol/L; 2) fasting plasma glucose >7.0 mmol/L; or 3) plasma
glucose >11.1 mmol/L two hours after intake of 1.75 g/kg of body weight [3].
T1DM can result from various disease processes, but for the majority of cases, the illness
develops as a result of the body’s own immune system destroying the insulin-producing beta
cells in the islets of Langerhans in the pancreas. This variety of diabetes is labelled type 1A
diabetes mellitus and accounts for approximately 80% of cases [11]. The immune process can
take several years to completely destroy the beta cells, with clinical symptoms only becoming
apparent when the process is in an advanced stage [12]. When clinical symptomatology is
characteristic of T1DM, but antibodies are absent in the blood, the variety of diabetes is
classified as idiopathic type 1B diabetes mellitus [3,11].
Metabolic control is a critical outcome measure in diabetes management, as good metabolic
control can prevent both acute and long-term complications, such as kidney failure, retinopathy,
heart disease and stroke. A blood measure that is widely used and accepted is the glycosylated
haemoglobin (HbA1c). The principle of HbA1c is based on the chemical reaction between glucose
molecules circulating the blood and haemoglobin in erythrocytes (red blood cells), forming
glycosylated haemoglobin. Once a haemoglobin molecule is glycosylated it remains in the
erythrocyte for the rest of its life. Because the average life-span of an erythrocyte is 120 days, a
measure of HbA1c gives an indication of the average plasma glucose concentration over the last
two to three months [13]. In 2009 the International Expert Committee [14] recommended the
use of an HbA1c cut-off value ≥48 mmol/mol for the diagnosis of diabetes. The ideal target for
metabolic control is an HbA1c value <42 mmol/mol, but for most diabetic patients the target is
set at 53 mmol/mol. Despite the available treatments and established benefits of lowering
glucose levels, only half the patients manage to keep their HbA1c levels <64 mmol/mol, and there
are only a handful of patients that manage to attain the goal of an HbA1c <53 mmol/mol [15].
This is worrying, since several studies have suggested that HbA1c levels >80 mmol/mol can
increase risks of complications [16-19].
Complications – acute and late-onset
Insulin is vital for glucose transport and cellular uptake. In diabetes with permanent insulin
deficiency, the level of glucose circulating the blood builds up and body cells are denied a direct
supply of nourishment. The body begins to use its own tissues as a power source, and cortisol,
glucagon and catecholamine levels increase as a counterbalance, accelerating gluconeogenesis
and glycogenolysis [20]. To provide the brain with energy, toxic ketones are produced by
breaking down fat molecules, leading to an increased risk of acute diabetic ketoacidosis (DKA)
[11]. These processes lead to excess dehydration, severe electrolyte imbalances, and metabolic
acidosis. Between 15% and 67% of children with T1DM in North America and Europe present
with DKA at diagnosis, making it a significant cause of morbidity and, in some cases, mortality in
children [21]. Next to the relatively shorter term complications of DKA, high glucose levels in the
blood can have longer term deteriorative effects on the body. Risks of microvascular
complications, such as retinopathy, neuropathy and nephropathy all seem to increase with
worse glycaemic control. A similar increased risk is seen for macrovascular complications, such
as cardiovascular disease, cerebrovascular disease and stroke, of which atherosclerosis is the
central pathological mediator [22]. The Diabetes Control and Complications Trial [17] showed
that intensification of diabetes treatment significantly decreased risks of microvascular
complications in adolescents, demonstrating the beneficial effects of intensive diabetes
management.
Intensive management of diabetes can result in increases in the number of hypoglyacemic
episodes. Hypoglycaemia is characterized by blood glucose levels <3.9 mmol/L and the
accompanying neurogenic and neuroglycopenic reactions of the body this brings about.
Neurogenic symptoms include sweating, trembling, tachycardia, pallor, hunger, arousal,
paresthesias, headache and nausea. Neuroglycopenic symptoms include fatigue, concentration
problems, blurred vision, slurred speech, feelings of warmth, confusion, emotional lability,
seizures, and death. Hypoglycaemia is easily resolved by consuming a glucose tablet or, when
consciousness is lost, by administering an intramuscular injection of glucagon. Although longlasting hypoglycaemia can cause permanent brain damage and even death, most patients fully
recover after a hypoglycaemic episode [23,24]. Because of the unpleasant bodily effects which
are not easily hidden from public, hypoglycaemia can be psychologically very stressful and cause
social difficulties. Fear of hypoglycaemia can cause patients to keep their glucose levels at a
synthetic high point to prevent future episodes, with all the consequences for health outcomes in
the long term [25,26].
Self-management in childhood and adolescence
To diminish the risk of neurological and vascular complications associated with hyperglycaemia,
patients are required to follow a strict regimen to maintain glucose control as near to normal as
is safely possible. This includes insulin replacement therapy, blood glucose monitoring and
watching diet and exercise patterns. Two different insulin replacement strategies are currently
widely used: 1) multiple daily injections (MDI) with three or more injections per day using
rapid-acting and slow-acting insulin variants; and 2) continuous subcutaneous insulin infusions
(CSII), also regularly referred to as pump therapy [27]. Several studies suggest that CSII therapy
results in greater metabolic control, compared to MDI therapy [28-30], although some evidence
suggests there is no significant difference in metabolic control between the two methods
[31,32].
Infants and most toddlers with diabetes are completely dependent upon their parents for
managing their diabetes. Undetected hypoglycaemia is the greatest risk in this period. Most
children under the age of 8 years old can participate in self-regulation tasks, although parents
and other caregivers are still most important for the child’s diabetes control. Children from 8 to
11 years old can take on more of the daily tasks concerning their diabetes, but still need
supervision from an adult. By the time of adolescence, children are more or less competent to
perform daily diabetes tasks individually, but still require the assistance of an adult for decisions
about insulin adjustments [27].
Keeping good metabolic control is especially important during puberty. Not just to prevent longterm microvascular complications, but also for normal growth and sexual maturation. Yet,
several studies have shown that children and adolescents with T1DM have difficulties in
managing their diabetes in such a way to ensure good levels of glycaemic control [33].
Particularly the transition from child to adolescent is accompanied by notable declines in
diabetes management and glycaemic control [34].
Adolescence is a period that is characterized by biological, cognitive, physical and emotional
changes. Hormonal and physiological changes accompanying the beginning of puberty influence
glucose and insulin sensitivity, increasing the risk of insulin resistance even before onset of
puberty [35]. Although this relationship has been well established in research, the exact
mechanism behind it is not clear [36]. Possible causative factors are located in the increase of
body fat [35,37], sex hormone changes [38], increases in levels of stress [39], and higher levels of
growth hormone. Both catecholamines and estrogen have an antagonistic influence on insulin’s
functioning and can increase the risk of insulin resistance [34,40]. Growth hormone is also an
important contributor to insulin resistance in puberty [34,41].
Important developmental and psychological changes in puberty concern the search for more
autonomy, the increased need for privacy, the importance of peer groups and changes in life
style and eating habits [40,42]. All these changes can place extra demands upon the adolescent
both emotionally and physically. It is not surprising that puberty can have significant impact on
self-care behaviour and metabolic control of adolescents with diabetes.
Anxiety
Anxiety disorders are classified in diagnostic systems such as the Diagnostic and Statistical
Manual of Mental Disorders [43] or the International Classification of Diseases [44].
Classifications of anxiety disorders found in both systems are: panic disorder (with or without
agoraphobia), agoraphobia, specific phobia, social phobia, generalized anxiety disorder (GAD),
obsessive-compulsive disorder (OCD), posttraumatic stress disorder (PTSD), acute stress
disorder, separation anxiety disorder, and categories for anxiety due to specific (medical or
psychiatric) causes and for subclinical presentations (anxiety NOS: Not Otherwise Specified).
Anxiety disorder classifications from both systems vary in their specific symptomatology,
severity and progression, but they have in common that all include manifestations of
pronounced fear and anxiety, physiological anxiety symptoms and behavioural disturbances,
such as avoidance and apprehension [45].
Studies investigating gender differences in mental health have found girls to be more anxious
than boys [46,47]. It is important to investigate the specific role gender plays in connection to
the association between anxiety and glycaemic control. Although girls seem to be at a greater
risk at experiencing anxiety, it is not clear if the connection between anxiety and glycaemic
control could be generalized to both sexes, or if it predominantly demonstrates an effect of girls.
Because healthy girls also experience more anxiety, the gender differences in youths with T1DM
could simply reflect normal developmental processes, and not have any significant effects on
HbA1c levels [48].
Several studies have attempted to throw light upon the relationship between anxiety and
diabetes. There are two hypotheses concerning the nature of this relationship. In the first view,
the physiological impact of diabetes is seen as a cause for anxiety, while in the second view, the
stresses associated with having diabetes are considered to cause anxiety symptoms. Studies
even indicate that depression or anxiety increase the risk of developing diabetes [49]. In line
with this thought is the proposition that trait anxiety and worries about diabetes may have a
negative influence on self-care behaviour and glycaemic control [5052]. This line of thought is
also seen in studies that suggest that fear for future hypoglycaemic episodes may lead to more
stress and anxiety [53]. The completely opposite direction of this relation is seen in the
protective effect theory. Some scholars suggest that anxiety could have a protective effect on
disease development, increasing patent compliance and disease management [54-56].
Current study
Clearly, children and adolescents with diabetes have to deal with various issues in their lives and
have different ways of dealing with their diabetes. Nevertheless, it is clear that keeping a good
level of glycaemic control is important for preventing complications in the long term. Anxiety
seems to be an important mediating factor in the relation between childhood, adolescence, and
metabolic control. This study will systematically review the existing evidence focusing on the
association between anxiety symptoms and metabolic control in children and adolescents with
T1DM. Special attention will be paid to the two theoretical directions in the relationship
between anxiety and glycaemic control, focusing on the evidence supporting both the
exacerbating effect and the protective effect of the association between anxiety and glycaemic
control. Attention will also be paid to the differences that exist between boys and girls and
different age groups, to identify possible influences of adolescence.
Method
Criteria for inclusion and exclusion
Articles were considered eligible for inclusion if they satisfied the selection criteria. The included
articles were limited to articles 1) published in English, Dutch, and German; 2) studying human
subjects; 3) studying child and adolescent samples (≤18 years of age); 4) assessing glycaemic
control using a measure of glycosylated haemoglobin (HbA1c); 5) measuring anxiety and HbA1c at
the same time.
Excluded were studies that 1) did not meet the inclusion criteria; 2) studied subjects with
glucose disorders other than type 1 or type 2 diabetes, such as gestational diabetes or impaired
glucose intolerance; 3) did not make an association between anxiety and HbA1c; 4) influenced
anxiety or HbA1c by placing subjects in treatment conditions; 5) measured anxiety in
combination with other psychiatric disorders; 6) measured anxiety in the caregivers of the
children and adolescents, instead of anxiety in the youths themselves.
Search strategy and selection
Computerized psychological and medical databases were searched to retrieve studies that
evaluated glycaemic control in connection to anxiety in diabetic children and adolescents. The
databases searched were PsycINFO, PsycARTICLES, Psychology & Behavioral Sciences
Collection, The Cochrane Library and PubMed. The search strategy was adjusted to the search
methods prescribed by each database, and was composed of the following search terms (*
indicates truncation):
diabet* AND (anxiety OR emotion* OR distress OR mental OR "well-being"
OR psychological OR psychosocial OR psychiatric OR fear OR panic OR OCD
OR obsessive-compulsive OR anankastic personality OR PTSD OR stress
disorder OR post-traumatic OR posttraumatic OR phobia OR neuroses OR
neurosis OR GAD OR generalized anxiety disorder) AND (hba1c OR a1c OR
glycemic OR glycosylated)
The reference lists of the included research and review articles were examined for additional
relevant articles.
Search results were entered into the review software EPPI-Reviewer 4. With the use of this
software duplicates were removed. A first selection of articles was made by screening the titles
and abstracts (if available) for potentially relevant articles. The full texts of this first selection
were obtained whenever possible and a second selection was made by screening the complete
articles. When articles seemed to meet inclusion criteria they were selected for data extraction.
When articles could not be retrieved through the Internet databases, the authors were contacted
by e-mail for a copy of the article. If contact data were not available, if the author did not reply or
if the author could not provide a copy of the article, the article was excluded. Figure 1 gives an
illustration of the selection process in the form of a flow diagram.
Quality assessment
According to a recent investigation by Ross and colleagues [57] there are a multitude of quality
assessment tools for observational studies. Although a number of useful tools are recognized,
there is not one tool applicable to all observational studies [58]. Fortunately there is now
widespread consensus over the guidelines for reporting observational studies. These guidelines
were set up by the STROBE (Strengthening the Reporting of Observational Epidemiological
studies) research group and give authors direction in better presenting their data [59]. For this
review the methodological quality of the included studies was assessed with the help of a
checklist based on the Downs and Black scoring method [60] and the adjustments made by
Gaynes and colleagues [61]. The checklist assesses quality of reporting, external validity
(generalizability), internal validity (bias and confounding) and precision. Each study was
assessed against nineteen questions, most of which were to be answered with ‘yes’, ‘no’, ‘unable
to determine’ or ‘not applicable’. The possible scores per question vary between 0 and 3 points,
with most questions yielding scores of 0 to 1. The maximum possible score is 24 points,
indicating superior quality. Studies scoring thirteen points or higher (54% or higher) were
deemed to be of sufficient quality. Studies scoring lower than 54% were included in the study,
but were given less weight in the analysis. The checklist questions are provided in Appendix A.
Statistics
To get a better view of the true effects of the included studies, effect sizes were calculated. For
the included studies that did not (solely) use regression methods, relevant results were
extracted and converted to an effect size r that was equal to Pearson’s correlation or
transformed from F or p values using an online effect size calculator [62]. Pearson’s r can vary
between -1 and +1. For the interpretation of these effect sizes Cohen [63] gives the following
guidelines: (-)0.10 = small effect; (-)0.30 = medium effect; and (-)0.50 = large effect. When
studies produced more than one effect, these were pooled to compute an average effect size.
Data Extraction
Articles were searched for relevant information with the use of an extraction table. When
articles that were selected for data extraction did not meet inclusion criteria, they were
discarded. Both qualitative and quantitative information was gathered. General information
regarding the article was gathered in the form of year and country of publication. With regard to
the participants the number (N), mean age and range in years, gender distribution, social
economic status (SES), disease duration and ethnicity were recorded. Concerning the method
used by the authors, the anxiety assessment method and the presence of a control group were
documented. The two most important outcome measures for this review were HbA1c levels and
anxiety scale scores which were both extracted together with the overall results.
Results
Quality of included studies
Overall the quality of assessed studies ranged from 42% to 79% (Table 1). As can be seen in
Figure 2 most studies scored reasonably well, achieving 50% or more of the possible 24 points.
There are just three studies that obtained scores less than thirteen points, with more than half of
the assessed studies scoring fifteen points or higher.
Figure 3 outlines the proportion of scores that satisfy the qualitative specifications as reviewed
by the questionnaire. In general the studies scored well on a number of quality assessing
questions. With regard to quality of reporting, most studies (95%-100%) clearly described their
objectives, methods and main findings. Participant characteristics and possible confounding
factors were clearly described in many studies as well (85% and 100% resp.), although studies
reported to have adjusted for confounders in only 60% of all cases. Only 60% of studies reported
the characteristics of participants lost to follow-up. This hiatus is also seen in the adjustments
made for subjects lost to follow-up, with only 40% of studies reporting to have done this.
Although representativeness of participants and location of assessment were satisfactory (both
70%), participants were not always recruited in the same time period (not in 60% of studies)
plus various studies did not supply proportions of subjects wanting to participate compared to
subjects not wanting to participate (not in 40% of studies). The statistical tests used were
appropriate in all included studies. Clinical interviews to verify anxiety classification and blood
tests for HbA1c measurement were not always used in conjunction, with simply one of the
methods used in the majority of studies (65% using one method; 10% using both methods).
Characteristics of included studies
The twenty included studies were conducted in the time period of 1981 through 2011. Most of
these studies were performed in North America and Europe (Sweden, Greece and the United
Kingdom), with one exception of a study from Kuwait. Where mentioned in the studies, most of
the populations were Caucasian, with smaller percentages of Black, Hispanic and other
ethnicities. The study by Naar-King and collegues [64] is an exception, with most (61%) of the
sample being Black. Sample sizes ranged from 23 to 349, with an average sample size of 93 and a
total population of 1875 investigated for this review. The mean age of youths varied from 8.6 to
16.1 years, with a range of 4.9 to 19 years. Disease duration varied from less than a year to 8.3
years. Some studies investigated aspects of the social economic status (SES) of the subjects
involved in the study, indicating a trend toward lower to higher middle class families. Excluding
three articles, most studies did not use a control group to validate their results.
In most studies HbA1c levels were assessed using blood analysis techniques, such as enzymatic
assays, high performance liquid chromatography, and DCA 2,000+. Some studies used selfreport to identify HbA1c levels. Anxiety was assessed using self-report questionnaires, with some
studies complementing these results with an interview. The most used scale was the validated
State Trait Anxiety Index (STAI), which gives an indication of the level of state and trait anxiety
of the participant. Next to general anxiety and fear, manifest anxiety, trait anxiety and state
anxiety, more specific anxieties such as social anxiety, injection and needle anxiety and fear of
hypoglycaemia were assessed by the included studies. For an overview of the characteristics of
the included studies, see Table 2.
Some studies directly calculated and reported the correlation (Pearson's r) between HbA1c levels
and anxiety. For twelve studies (pooled) effect sizes could be computed or extracted from the
data. Five of the studies did not report a significant result, so were coded as having a
nonsignificant or zero effect size. The effect sizes and some other characteristics of these
seventeen studies are reported in Table 3. Eight of the studies used regression methods, of
which the relevant data are reported in Table 4.
The adverse relationship between anxiety and HbA1c levels
In eighteen of the studies the positive relationship between anxiety and HbA1c was examined.
Investigated was if participants scoring high on anxiety also had high HbA1c levels, and vice
versa. The effect sizes of studies that found a relationship ranged from 0.11 to 0.58.
Four studies [39,65-67] used threshold levels of HbA1c severity to construct categories, and
divided participants among these categories. These categories were used as individual groups
that could be compared to eachother. Anderson and colleagues [65] regarded levels <86
mmol/mol as ‘good’ glycaemic control, levels of 86-130 mmol/mol as ‘fair’ glycaemic control,
and levels >130 mmol/mol as ‘poor’ glycaemic control. Delamater and colleagues [39]
considered levels <86 mmol/mol as ‘good’ glycaemic control, levels of 86-108 mmol/mol as ‘fair’
glycaemic control, and levels >108 mmol/mol as ‘poor’ glycaemic control. Moussa, et al. [66]
considered levels <64 mmol/mol ‘good to excellent’ glycaemic control, levels of 65-86
mmol/mol ‘fair glycaemic control’, and levels >86 mmol/mol ‘poor’ glycaemic control. Simonds
and colleagues [67] identified participants as having ‘high’ HbA1c when exceeding 80 mmol/mol,
and as having ‘low’ HbA1c when their HbA1c did not exceed 80 mmol/mol. Two of these studies
[65,66] found a significant difference between their assigned groups on HbA1c levels, with poorly
controlled youths scoring higher on the anxiety measure than their better controlled peers
(resp. p = 0.001 & p = 0.022). Both these studies were judged to be of good methodological
quality, reaching scores of 75% [65] and 79% [66]. Delamater, Kurtz, Bubb, White & Santiago’s
research [39] did not replicate these results and found that anxiety was unrelated to metabolic
control (F (2,24) = 0.452, p = 0.642). However, when recalculating the reported F value to an r
value, there does seem to be a small to medium sized effect (r = 0.19) present between the
glycaemic control groups. The major differences lie between the groups with good and fair
glycaemic control and between the groups with poor and fair glycaemic control. However,
conclusions must be made with caution, because the assessed quality of this study reached a
score of just 50%. Simonds, Goldstein, Walker & Rawlings [67] examined the relationship
between manifest anxiety and HbA1c levels, but did not find a significant relationship (F = 0.60).
This study had a quality score of 63%, being of average quality.
Herzer & Hood [68] found that higher state anxiety scores were related to poorer glycaemic
control (r = 0.25). Trait anxiety scores did not seem to be significantly correlated to HbA1c levels
(r = 0.11). When they divided participants over groups of low, moderate and high anxiety scores,
they found significant differences between the three groups, but only for state anxiety scores (F
(2,273) = 11.28, p < 0.01). When they added state anxiety to a regression model containing
sociodemographic, family, disease and depression data, HbA1c values seemed to be associated
with higher state anxiety (p = 0.002). This study was of good quality, with a score of 75%.
Four other studies also attained results indicating a positive relation between anxiety and HbA1c
[69-72]. In Herzer, Vesco & Ingerski’s study [69], participants were followed for nine months,
with measurements made at baseline, six months and nine months. Baseline anxiety scores were
associated with nine-month HbA1c levels (r = 0.17, p < 0.05) and six-month anxiety scores were
associated with nine-month HbA1c levels (r = 0.27, p < 0.0001). In a regression analysis
containing family conflict, anxiety and HbA1c values, anxiety accounted for 20% of the
relationship between family conflict and HbA1c (β = 0.21, p < 0.01). This study was deemed to be
of sufficient quality, with a score of 58%. The studies by Hilliard, Herzer, Dolan & Hood [70] and
Lernmark, Persson, Fisher & Rydelius [71] were considered to be of good quality, with scores of
67% and 71% respectively. The first of these studies found that higher state anxiety scores were
significantly associated with higher HbA1c levels, both at baseline (r = 0.30, p < 0.001) and at 12month follow-up (r = 0.25, p < 0.01). Higher STAI scores were significant predictors (β = 0.07, p <
0.05) in a multiple regression model. A 14-point increase in anxiety scores was associated with a
1% increase in the HbA1c level. Lernmark, Persson, Fisher & Rydelius’ study [71] suggests that
there is a significant association between fear and HbA1c levels (r = 0.25, p < 0.05). However, a
multivariate regression analysis containing age, gender, adaptation, depression, self-esteem and
fear, did not show fear to be a significant predictor of glycaemic control. Skinner & Hampson
[72] did find a significant relationship between general anxiety scores and worse HbA1c levels (r
= 0.26, p = 0.05) when they followed a group of 54 youths with T1DM for a year. Although they
used a small sample, this study was awarded a quality score of 67%, being of satisfactory quality.
Two other studies [64,73] that dealt with the positive relationship between anxiety and HbA1c
are of poor and mediocre quality, with scores of 46% and 54%, so caution is warranted when
interpreting these results. Urquhart Law, Kelly, Huey & Summerbell [73] found that higher HbA1c
levels were significantly correlated to higher anxiety scores (r = 0.58, p = 0.03). Naar-King et al.
[64] found that self-reported anxiety was not significantly correlated to HbA1c levels (r = 0.12, p
> 0.05). The correlation coefficient of 0.12 does show that there is some effect, albeit small.
Six other studies did not find a significant relationship between anxiety and HbA1c, although the
types of anxiety under investigation differed. Di Battista, Hart, Grezo & Gloizer [74] found no
significant correlation between social anxiety and glycaemic control. Both variables were
collected with the use of self-report. Gonder-Frederick, et al. [54] did not find a significant
correlation between both fear of hypoglycaemia and glycaemic control, and between trait
anxiety and glycaemic control. However, they also found that youths with T1DM who had
experienced a severe hypoglycaemic episode in the past had worse glycaemic control than other
youths with T1DM. Howe, Ratcliffe, Tuttle, Doughert & Lipman [75] examined the relationship
between self-reported fear and HbA1c levels, but found no significant result (p > 0.10).
Liakopoulou, Korvessi & Dacou-Voutetakis [76] investigated manifest anxiety and its role on
HbA1c levels, but also found no significant relationship. They did find a connection between
participants who worried more about their diabetes and HbA1c levels (p = 0.021). All of these
four studies were of medium quality, scoring 58% and 63%. Ingerski, Anderson, Dolan & Hood
[77] and La Greca, Swales, Klemp, Madigan & Skyler [48] both executed various regression
analyses, but found no significant connections between anxiety and glycaemic control. The
quality of these studies was medium to good, with scores of 75% and 63% respectively.
The protective relationship between anxiety and HbA1c levels
Three studies found evidence of a negative relationship between anxiety and HbA1c. Participants
scoring high on anxiety had lower HbA1c levels, and vice versa. Vukovic, et al. [78] found
evidence for the existence of this negative correlation between anxiety and HbA1c (r = -0.239, p =
0.036). To study the relation between glycaemic control and several other variables, such as
coping, time structuring capacity and depression, anxiety was entered into a multiple regression
analysis. The results indicated that anxiety was a significant predictor of metabolic control (β =
0.262, p = 0.0006). When anxiety scores were higher, glycaemic control was better. This study
did not have sufficient quality, only scoring 42%. Lane, et al. [79] did not confirm the results of
Vukovic, et al. [78]. They found a nonsignificant association between trait anxiety scores and
metabolic control (r = -0.02, p > 0.9). The study was of mediocre quality, with an acquired
quality score of 54%. Thernlund, et al. [80] performed a logistic regression with glycaemic
control as dependent variable and achieved a model in which child injection anxiety seemed to
predict better glycaemic control (χ2 = 9.93, p < 0.01). This study was of good quality, scoring
75%.
Influence of age
The mean age in the samples of the reviewed studies lay between 8.6 to 16.1 years (range 4.9 to
19 years). Although this seems to cover both children and adolescents, the bulk of the samples
was aged older than 11 years of age. Five out of twenty studies [64,66,71,75,80] clearly stated
that they investigated samples of children younger than 11 years of age. Other studies did not
provide age ranges or only investigated teens.
Howe, Ratcliffe, Tuttle, Doughert & Lipman [75] found that children younger than 9 years of age
reported more fear of injections than older children (p = 0.026). This did not seem to affect their
HbA1c, because the correlation was nonsignificant (p > 0.10). Lane and colleagues [79]
investigated a sample of 12 to 19 year-olds and found that there was no significant association
between age and HbA1c (r = 0.17, p < 0.35). Thernlund, et al. [80] divided participants over three
age groups: pre-school (0-5 years), latency (6-11 years) and puberty (>10 years). They found
that the youngest and the oldest age groups significantly differed in their mean HbA1c levels (p =
0.011), with younger children having better glycaemic control than the older children. These
results were not combined with anxiety. Ingerski, Anderson, Dolan & Hood [77] found that
glycaemic control was worse for older adolescents. They added age as a covariate to their
multiple regression model with HbA1c as dependent variable, but the association with anxiety
was not further investigated. Lernmark, Persson, Fisher & Rydelius [71] also added age as a
covariate to a regression model with HbA1c as dependent variable, further containing gender,
adaptation, depression, self-esteem and fear as variables. No effect was found for age on HbA1c.
Influence of gender
Most of the included studies investigated differences between boys and girls in anxiety scores or
HbA1c levels, and some even looked at both. Only some of the reviewed studies investigated
whether gender had a significant role in the relationship between anxiety and HbA1c. In relation
to this, many of the included studies did show that girls with T1DM seem to have higher levels of
anxiety than boys with T1DM [48,53,64-67,71-74] and higher levels of HbA1c [48,65,67,71,72].
Di Battista, Hart, Greco & Gloizer [74] found that there was no gender difference in the relation
between social anxiety and HbA1c values. Together with state anxiety scores, Herzer & Hood [68]
added gender as a variable in their multiple regression model with HbA1c as dependent variable .
The overall model model was significant (F (15,260) = 7.97, p < 0.0001), but gender was not. In
five studies [66,69-71,77] gender was also added as a covariate to a multiple regression model,
but its individual effect was not further investigated. La Greca, Swales, Klemp, Madigan & Skyler
[48] did look specifically at gender in connection to anxiety and HbA1c levels in youths with
T1DM. Although they found that girls exhibited poorer glycaemic control (F = 4.74, p < 0.05), and
more trait anxiety (F = 6.17, p < 0.05) than boys, the four regression analyses they performed
with combinations of HbA1c, depression, anxiety, gender and duration of disease, did not show
anxiety to be a significant predictor of HbA1c and vice versa. Skinner & Hampson [72] added
gender, SES, diet and anxiety into a stepwise multiple regression analysis, resulting in an
indication that gender was a significant predictor of glycaemic control (β = -0.45, p < 0.05).
Although Gonder-Frederick et al. [53] did look at an interaction term of anxiety*gender, this was
not in association with its effects on HbA1c levels. Naar-King et al. [64] also investigated the role
of gender in the relationship between anxiety and HbA1c and demonstrated with a structural
equation model that the indirect effect of gender on HbA1c through anxiety was nonsignificant.
Discussion
The objective of this study was to gain more insight into the relationship between anxiety
symptoms and metabolic control in children and adolescents with T1DM. Special attention was
paid to both the exacerbating and the protective aspects of this relationship.
Because of the correlational nature of the reviewed studies, no statements could be made
regarding the causal direction of the relationship between anxiety and HbA1c levels. With regard
to the nature of the relationship, most evidence found in this review seemed to point to a
relationship of exacerbation. Only two studies found a significant relationship in which there
seemed to be a protective effect. One of these studies found a direct correlation and the other
found that child injection anxiety was a significant predictor of better glycaemic control with the
use of a logistic regression analysis. The study that found the direct correlation was of poor
quality, leading to a cautionary stance towards these results.
Direct exacerbating relations between anxiety and glycaemic control were found in eleven
studies, which were all of medium to good quality. Effect sizes ranged from 0.11 to 0.58. Four of
these studies divided participants into groups of different levels of metabolic control. Although
half of these studies found significant differences between glycaemic control groups, the other
half did not replicate this result. Because all four studies used different threshold values to
define their glycaemic control groups. some of the differences in these results could be explained
by the different interpretations the studies gave to ‘good’, ‘fair’ and ‘poor’ glycaemic control.
Seven studies found no significant correlations at all. However, these studies did suggest the
presence of associations between more specific anxiety-related events and HbA1c levels.
Reported examples were the experience of a severe hypoglycaemic episode and diabetes related
worries, which were both connected to a rise in HbA1c.
Attention was also paid to the influence of gender and age on glycaemic control. In most of the
reviewed studies girls with T1DM consistently reported to be more anxious than boys with
T1DM. However, most of these studies did not investigate the effect gender played in the
connection between anxiety and HbA1c. Gender was often added to regression models as a
covariate, but only a few studies investigated gender’s own effects on the relationship between
anxiety and glycaemic control. Di Battista, Hart, Greco & Gloizer [74], La Greca, Swales, Klemp,
Madigan & Skyler [48] & Naar-King, et al. [64] did look at its individual effects, but did not find
significant results. Skinner & Hadson [72] found that gender was a significant predictor of
glycaemic control by performing a stepwise multiple regression analysis.
The magnitude of the total sample under review consisted of children older than 11 years of age,
with most studies including a wide range of ages. Not all studies divide subject groups in
different age groups, resulting in possible flattening results. As has been shown, adolescence is a
time of great change in the adolescent, which also has its effects on metabolic control. Because
diabetes and metabolic control seem to be age dependent [81], this could form a potential bias. A
small number of studies (k = 5) clearly included younger participants. They found that children
aged 9 years or younger reported more fear than older children [75], that glycaemic control was
worse for older adolescents [77] and that children younger than 5 years of age had significantly
better HbA1c levels than children aged 10 years or older [80]. These results confirm data found
in the literature concerning age effects on glucose regulation in childhood and adolescent
diabetes [82-84]. However, age-dependent effects on the relationship between anxiety and
HbA1c were not investigated and most studies used wide age ranges and averaged the found
effects across age groups. So, although there did appear to be some evidence for age differences
in anxiety and HbA1c, because of the potential presence of bias this conclusion could not be
extended towards a general statement concerning the effects of age on the relation between
anxiety and HbA1c.
An important issue in this review was the heterogeneous assessment of anxiety. Reasons for the
variability in data may reside in the diversity in types of anxiety and the mixture of methods
used to assess anxiety across studies, making results dependent on the validity of used
questionnaires or interview techniques. Taken together, the reviewed studies examined a total
of eight different types of anxiety. To determine the level of anxiety per participant, various
questionnaires and interview techniques were used. The variety in anxiety assessment might
explain some of the different results found in this review. Although HbA1c levels were assessed
with the use of a blood test in most of the reviewed studies, an issue that should be taken into
consideration is that HbA1c levels do not give an indication of the fluctuation in glucose levels. A
patient could have a low HbA1c, by compensating very high blood glucose levels with very low
levels. This in itself can be detrimental for the patient’s health. Another limitation was the
method of quality assessment. Quality checklists for observational studies, such as the Downs &
Black checklist used as a basis in this review, are available in large numbers. Despite the
availability of these tools, these checklists vary in their views on the features of a study that are
deemed important for methodological quality. Although the Downs & Black checklist was
validated and ranked in the top six of quality assessment tools for observational studies [85], it
is important to realise the variance in opinion concerning quality assessment of observational
studies. A final issue to take into consideration is that half of the included studies were executed
more than a decade ago. Since that time, treatment has improved significantly and is still
improving every day, giving patients with diabetes more freedom and a better chance to live
healthier for a longer time. Although this could have a positive influence on the number of
anxiety symptoms reported by youths, intensification of the diabetes regimen also has to be
considered. With the introduction of better ways to regulate glucose levels, new demands are
placed upon the patient with diabetes for more attention and more active involvement in their
disease. Not all patients are equipped to do this, which could lead to more anxiety. Both these
developments are important to consider when interpreting the differences in number of anxiety
symptoms reported by youths.
This review has given a glimpse into the association between anxiety and HbA1c. The results
found in this study indicate that anxiety and HbA1c are related. However, no statements could be
made about the causal direction of this relationship. The relation does not appear to be a pure
one and varies between types of anxiety, age groups and gender. More knowledge is needed to
further examine the relationship between the different forms of anxiety and glycaemic control,
and its causal direction. Additional psychological factors that could play an indirect role in this
relationship should be taken into consideration. This should also include a further examination
of the contributions of gender and age in this relationship. Preferably, these examinations should
include participants of the same age, to create more homogenous groups and thus reduce
variation. Although most reviewed studies did not find an effect of gender or age in the
relationship between anxiety and glycaemic control, these results were not conclusive. Many of
the studies did not investigate the contributions of age and gender or lacked in methodological
quality. More insight into these relations could be helpful for diabetes professionals in assisting
children and adolescents with diabetes to better cope with their disease.
Finally, the clinical consequences should be considered. This review has underscored the need to
screen young patients with diabetes for anxiety. Considering the impact of anxiety on the wellbeing and glycaemic regulation of patients, it is of considerable importance to offer
psychological treatment to young patients with diabetes when necessary.
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75. Howe, C.J., Ratcliffe, S.J., Tuttle, A., Dougherty, S. & Lipman, T.H. (2010). Needle Anxiety in
Children with Type 1 Diabetes and their Mothers. The American Journal of Maternal Child
Nursing, 36, 25-31.
76. Liakopoulou, M., Korvessi, M. & Dacou-Voutetakis, C. (1992). Personality Characteristics,
Environmental Factors and Glycaemic Control in Adolescents with Diabetes. European Child
and Adolescents Psychiatry, 1, 82-88.
77. Ingerski, L.M., Anderson, B.J., Dolan, L.M. & Hood, K.K. (2010). Blood Glucose Monitoring and
Glycemic Control in Adolescence: Contribution of Diabetes-Specific Responsibility and
Family Conflict. Journal of Adolescent Health, 47, 191-197.
78. Vukovic, D., Vlaski, J., Konstantinidis, N., Katanic, D., Konstantinidis, G. & Biroo, M. (2010).
Psychological Aspects of Adolescents with Diabetes Mellitus. Procedia Social and Behavioral
Sciences, 5, 1788-1793.
79. Lane, J.D., Stabler, B., Ross, S.L., Morris, M.A., Litton, J.C. & Surwit, R.S. (1988). Psychological
Predictors of Glucose Control in Patients with IDDM. Diabetes Care, 11, 798-800.
80. Thernlund, G., Dahlquist, G., Hägglöf, B., Ivarsson, S.A., Lernmark, B., Ludvigsson, J. & Sjöblad,
S. (1996). Psychologcial Reactions at the Onset of Insulin-Dependent Diabetes Mellitus in
Children and Later Adjustment and Metabolic Control. Acta Paediatrica, 85, 947-953.
81. Grey, M., Cameron, M.E. & Thurber, F.W. (1991). Coping and Adaptation in Children with
Diabetes. Nursing Research, 40, 144-149.
82. Johnson, S.B., Kelly, M., Henretta, J.C., Cunningham, W.R., Tomer, A. & Silverstein, J.H. (1992).
A Longitudinal Analysis of Adherence and Health Status in Childhood Diabetes. Journal of
Pediatric Psychology, 17, 537-553.
83. Mortensen, H.B., Robertson, K.J., Aanstoot, H.-J., Danne, T., Holl, R.W., Hougaard, P., ... Aman, J.
(1998). Insulin Management and Metabolic Control of Type 1 Diabetes Mellitus in Childhood
and Adolescence in 18 Countries. Diabetes Care, 15, 752-759.
84. Bryden, K.S., Peveler, R.C., Stein, A., Neil, A., Mayou, R.A. & Dunger, D.B. (2001). Clinical and
Psychological Course of Diabetes From Adolescence to Young Adulthood: A Longitudinal
Cohort Study. Diabetes Care, 24, 1536-1540.
85. Deeks, J.J., Dinnes, J., D’Amico, R., Sowden, A.J., Sakarovitch, C., Song, F., … Altman, D.J. (2003).
Evaluating Non-Randomised Intervention Studies. Health Technology Assessment, 7, 1-179.
Table 1
Quality of included studies
Total
Questions
Study
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
Sum
Prop.
Anderson (1981)
1 1 0 1 1 1 0 1 1
1
1
2
1
1
1
1
1
1
1
18
0.75
Delamater (1987)
1 1 0 2 1 1 1 0 0
0
1
1
1
1
1
0
0
0
0
12
0.50
Di Battista (2009)
1 1 1 2 1 1 0 1 1
0
1
0
1
1
0
1
1
0
1
15
0.63
Gonder-Frederick
1 1 1 2 1 1 0 1 1
0
1
1
0
1
1
0
1
0
1
15
0.63
Herzer (2010)
1 1 1 2 1 1 0 2 1
1
1
1
1
1
1
0
1
0
2
18
0.75
Herzer (2011)
1 1 1 1 1 1 0 1 1
0
1
1
1
1
1
0
0
0
1
14
0.58
Hilliard (2011)
1 1 1 1 1 1 0 1 1
0
1
1
1
1
1
1
0
1
1
16
0.67
Howe (2011)
1 1 1 1 1 1 0 1 1
0
1
0
1
1
1
1
0
0
1
14
0.58
Ingerski (2010)
1 1 1 2 1 1 1 1 0
1
1
1
1
1
1
0
1
1
1
18
0.75
La Greca (1995)
1 1 1 2 1 1 0 1 1
1
0
1
1
1
1
0
0
0
1
15
0.63
Lane (1988)
1 1 1 1 1 1 0 0 1
0
1
1
1
1
1
0
0
0
1
13
0.54
Lernmark (1999)
1 1 1 1 1 1 1 1 1
1
1
1
0
1
1
0
1
1
1
17
0.71
Liakopoulou (1992)
1 1 1 2 1 1 0 1 0
0
0
2
1
1
1
0
0
0
1
14
0.58
Moussa (2005)
1 1 1 2 1 0 1 1 1
1
0
1
1
1
1
1
1
1
2
19
0.79
Naar-King (2006)
1 1 1 2 0 1 1 0 0
0
0
1
0
1
0
0
1
0
1
11
0.46
Simonds (1981)
1 1 1 1 1 1 1 0 0
0
1
1
1
1
1
0
1
1
1
15
0.63
Skinner (2001)
1 1 1 1 1 1 1 0 1
1
1
0
0
1
1
1
1
1
1
16
0.67
Thernlund (1996)
1 1 1 1 1 1 1 1 1
1
0
1
1
1
1
1
1
1
1
18
0.75
Urquhart (2002)
1 1 1 1 1 1 0 1 1
0
1
0
0
1
1
0
1
0
1
13
0.54
Vukovic (2010)
1 0 0 2 1 1 0 1 0
0
0
0
1
1
0
1
0
0
1
10
0.42
(2006)
Note. Prop. = proportion of sum score/24. Possible scores per question are 0-4, with 0 indicating absence of quality
requirement or unable to determine, 1 indicating presence of (one or partial) quality requirement, 2 indicating
complete presence of quality requirements (in questions 4 and 12), and scores 3 and 4 indicating a big sample size
(scores 3 and 4 are only used in question 19).
Table 2
Characteristics of included studies
Participants
Study
N
Anderson (1981)
58
Method
Mean age
Disease
& SD
duration
range
Gender
Country and
(years)
(F : M)a
ethnicity
-
30 : 28
11-19
SES
North America
-
Middle to
Outcomes
(mean
Anxiety
Control
years &
assessme
Group
SD)
nt
Y/N
5.2
PH-CSCS
Summary of results
Anxiety scale
HbA1cb
No
scores
Mean: 11.9 %
Good: 10
Significant: poorly controlled
upper-middle
Anxiety
(107) ± 2.7
Fair: 8.6
adolescents with T1DM reported
class
subscaleb
‘good’ <86: n=14
Poor: 7.2
more anxiety than better
‘fair’ 86-130:
controlled adolescents with
n=21
T1DM
‘poor’ >130:
n=12
Delamater (1987)
27
15,4 ± 1.6
11 : 16
North America
Primarily
7.1 ± 3.6
STAI
No
Mean: 98
Good: 33.5 ±
Nonsignificant: No significant
Range: 48-139
14.8
group differences metabolic
White: 22
‘good’ <86: n=8
Fair: 37 ± 6.9
control-anxiety
Black: 5
‘fair’ 86-108: n=9
Poor: 31.5 ±
‘poor’ >108:
14.7
middle class
-
n=10
Di Battista (2009)
76
15.9 ±
1.44
13-18
43 : 33
North America
Mean
6.42 ±
SAS-A
/ Canada
household
3.63
HFS
No
8.90 % (74) ±
37.73 ± 11.70
Nonsignificant: No correlation
1.86
Girls: 39.37 ±
between HbA1c values and social
income:
Girls: 8.88 % ±
1.43
anxiety
White: 64
40.000-
1.74
Boys: 35.59 ±
Black: 9
59.999
Boys: 8.93 % ±
11.88
Other: 3
2.04
Gonder-Frederick
39
15.36 ±
17 : 22
North America
1.53
(2006)
-
Mean years
7.03 ±
HFS-C
of education
4.00
STAIC
No
7.85 % (62) ±
HFS-C: 65.24 ±
Nonsignificant: Fear of
1.09
13.24
hypoglycaemia and trait anxiety
White: 27
parents: 15.2
STAIC: 31.19 ±
are not significantly associated
Black: 5
yr ± 2.9
7.38
with metabolic control.
Adolescents who had experienced
SH in the past had higher Hba1c
levels
Herzer (2010)
276
15.6 ±
131 :
1.39
145
13-18
North America
Caregivers:
6.6 ± 4.0
STAI(C)
No
8.9 (74) ± 1.8
STAI state
Mixed results: Higher levels of
77,9 %
anxiety: 29.8 ±
state and trait anxiety scores
Black: 22
married,
5.0
were significantly related to
White: 241
54% at least
STAI trait
poorer glycaemic control.
Hispanic: 7
college
anxiety: 32.1 ±
There are significant differences
Asian: 4
degree
7.0
in HbA1c levels between low,
Other: 2
moderate and high levels of state
anxiety, but no significant
differences were found for the
levels of trait anxiety. When state
anxiety scores were added to
their regression model HbA1c
values seemed to be associated to
higher state anxiety
Herzer (2011)
147
15.5 ± 1.4
13-18
76 : 71
North America
STAIC state
Significant: Both baseline and 6-
75,5 %
anxiety: 29.7 ±
month anxiety scores were
Black: 16
married,
4.8
significantly correlated with 9-
White: 127
46.9% at
month HbA1c levels.
Hispanic: 2
least college
In a regression analysis
Asian: 1
degree
containing family conflict,
Other: 1
Caregivers:
6.0 ± 3.8
STAIC
No
9.1 % (76) ± 2.1
anxiety and HbA1c values, anxiety
accounted for 20% of the
relationship between family
conflict and HbA1c
Hilliard (2011)
150
15.5 ± 1.4
77 : 73
13-18
North America
Caregiver
6.0 ± 3.9
STAIC
No
8.8 % (73) ± 1.9
STAIC state
Significant: Higher anxiety
with at least
anxiety: 30.3 ±
scores are associated with higher
White: 129
college
5.2
HbA1c levels.
Other: 21
degree:
Higher STAI scores were
46.7%
significant predictors in a
multiple regression model. A 14point increase in anxiety scores is
associated with a 1% increase in
Hba1c.
Howe (2011)
23
9.9
11 : 12
4.9-16.2
North America
-
White: 21
Newly
FACES-
diagnose
d (<1 yr)
No
-
% Fear with
Nonsignificant: Child self-
PRS
injections/
reports of fear were not
VAS
fingersticks
significantly associated with
At diagnosis
HbA1c levels. Mother’s high
(young:older)
distress correlated with poorer
75.0/62.5 :
cooperation of the child and
21.4/14.3
higher HbA1c levels
Black: 2
After 6-9
months
(young:older)
28.6/28.6 :
0.0/0.0
Ingerski (2010)
147
15.5 ± 1.4
13-18
76 : 71
North America
Caregivers:
6.0 ± 3.8
76%
0.5-16.75
STAI
No
8.8 % (73) ± 1.9
-
Nonsignificant: No significant
relationship seems to exist
White: 126
married,
between state anxiety or trait
Other: 21
47% have a
anxiety and HbA1c levels. State
college
anxiety and trait anxiety had
degree
nonsignificant beta values when
added in the second block of a
regression model with Hba1c as
dependent variable.
La Greca (1995)
42
15.8 ± 1.8
25 : 17
North America
Middle- and
6.9 ± 4.4
STAI-R
No
9.35 % (79) ± 2.3
Girls: 35.00 ±
Nonsignificant: Different
8.8
regression analyses show that
White: 27
Boys: 28.73 ±
anxiety is not significantly related
Black: 7
5.2
to metabolic control. Boys and
working class
12-18
Hispanic: 8
Lane (1988)
31
15.3
5 : 26
North America
girls did differ in anxiety levels
-
-
STPI
No
109
-
49-162
12-19
Nonsignificant: Trait anxiety is
not significantly related to
-
metabolic control (r = -0.20, p <
0.9)
Lernmark (1999)
62
14
37 : 25
Europe –
-
Sweden
6.0
FSSC-R
No
7.9 % (63) ± 1.6
3-14
9-18
-
Fear girls: 101
Mixed results: There is a
± 57
significant correlation between
Fear boys: 57 ±
fear and metabolic control (r =
44
0.25). A linear regression analysis
did not show fear to significantly
predict metabolic control when
added together with adaptation
and gender
Liakopoulou
40
-
(1992)
23 : 17
Europe –
Upper middle
5.51 ±
CMAS
Yes (N
Greece
class (II &
3.65
Rutter-
= 39)
11-18
-
III): 29
Graham
Lower
interview
-
Mean CMAS
Nonsignificant: No significant
anxiety: 28.7 ±
correlation between anxiety and
6.4
HbA1c. The Worries subscale of
the psychiatric interview was
middle class
significantly correlated to HbA1c
(IV): 11
levels
(Hollingshea
d class II, III
& IV)
Moussa (2005)
349
13.41 ±
217 :
Middle East –
Low: 155
3.22
132
Kuwait
(44.4%)
6-18
Naar-King (2006)
119
13.3 ±
-
58 : 61
North America
1.89
9.9-16.8
-
HSCL-25
Yes (N
-
HbA1c≤64: 1.50
Significant: Children with higher
HbA1c 65-86:
HbA1c levels had higher anxiety
Medium: 167
1.90
scores compared to children with
(47.9%)
HbA1c >86:
good metabolic control.
High: 27
2.10
(7.7%)
Controls: 1.50
= 409)
Family
4.9 ± 3.09
income
1-13
BASC
No
11.38 % ± 2.3
Anxiety self-
Significant: The relationship
report (F:M):
between anxiety and HbA1c was
not significant
White: 31
<$25.000:
48.45 ± 9.35 :
Black: 73
50%
45.20 ± 7.93
Other: 15
Anxiety
caregiverreport (F:M):
50.76 ± 10.12 :
48.28 ± 10.09
Simonds (1981)
52
16.15
23 : 29
13-19
North America
All white
Mostly rural
8.3
middle class
Girls: 7.5
(Hollingshed
Boys: 9.1
CMAS
No
‘low’ <80: 25
‘low’: 5.92
Nonsignificant: There is no
‘high’ ≥80: 27
‘high’: 5.85
significant relationship between
Girls: 7.83
high and low HbA1c levels and
Boys: 4.34
anxiety scores.
Baseline/Follo
Mixed results: Anxiety is
2 factor: 3.21
(1-5))
Skinner (2001)
54
Parental
Girls: 5.0
WBQ
occupation
± 4.5
anxiety
w-up
correlated with HbA1c levels.
Professional:
Boys: 4.8
subscale
Girls: 6.6 ± 3.5
When dietary control is added to
Boys:
24.1%
± 3.0
/ 6.9 ± 3.4
the regression, this effect no
14.6 ± 1.9
Intermediate:
Boys: 3.6 ± 2.5
longer exists. This means dietary
14.5
25 : 29
Europe – UK
Girls:
14.4 ± 1.8
-
No
-
37%
-
/ 3.9 ± 2.3
control may mediate the
Skilled:
relationship between anxiety and
31.5%
metabolic control
Semiskilled/
unskilled:
7.4%
Thernlund (1996)
76
8.6 ± 3.9
38 : 38
Europe –
-
-
Sweden
SCQ
No
?
interview
-
Assessment
Significant: A logistic regression
child / staff
with glycaemic control as
Anxiety: 2.44 ±
dependent variable indicated that
1.31 / 3.11 ±
children with injection anxiety
1.23
and less generalized distress have
Injection
better metabolic control
anxiety: 1.88 ±
1.09 / 2.45 ±
1.10
Urquhart Law
30
15.5 ± 1.6
14 : 16
Europe – UK
-
4.9 ± 3.6
WBQ
No
0.3-13.9
(2002)
13-19
Vukovic (2010)
77
14.08
12-18
9.1 % (76) ± 1.4
-
48-102
Significant: Higher HbA1c was
related to higher anxiety levels
All white
-
-
-
5.88
CBCL
Girls: 6.5
Anxiety
anxiety score, the better
Boys:
subscale
metabolic control (subjects in
5.25
No
<58: 30
-
Significant: The higher the CBCL
good metabolic control show
more anxiety)
Note. a = Female:Male; b = (converted to) mmol/mol whenever possible, in other cases HbA1c percentages are given. PH-CSCS = Piers-Harris Children’s Self-Concept
Scale; STAI = Spielberg Trait Anxiety Inventory; SAS-A = Social Anxiety Scale for Adolescents; HFS = Hypoglycemia Fear Survey; CBCL = Child Behavior Checklist;
HFS-C = Hypoglycemia Fear Survey for Children; STAIC = Spielberg Trait Anxiety Inventory for Children; FACES-PRS = Faces Pain Rating Scale; VAS = Visual
Analogue Scale; STAI-R = Spielberg Trait Anxiety Inventory Revised; STPI = State-Trait Personality Inventory; FSSC-R = Fear Survey Schedule for Children; CMAS =
Children’s Manifest Anxiety Scale; HSCL-25 = Hopkins Symptom Checklist-25; BASC = Behavior Assessment System for Children; WBQ = Well-Being Questionnaire;
SCQ = The Staff Crisis Questionnaire
Table 3
Effect sizes
Study
Assessment method
Effect size (r)
general anxiety
(self-concept)
PH-CSCS Anxiety
0.44
0.75
27
trait anxiety
STAI
0.19
0.50
Herzer (2010)
276
STAI(C)
0.21
0.75
Herzer (2011)
147
STAIC
0.22
0.58
Hilliard (2011)
150
STAIC
0.28
0.67
62
state anxiety
trait anxiety
state anxiety
trait anxiety
state anxiety
trait anxiety
general fear
FSSC-R
0.25
0.71
Moussa (2005)
349
general anxiety
HSCL-25
0.11
0.79
Naar-King (2006)
119
general anxiety
BASC
0.12
0.46
54
general anxiety
WBQ anxiety
0.26
0.67
Anderson (1981)
Delamater (1987)
Lernmark (1999)
Skinner (2001)
N
58
Type of anxiety
Quality (prop.)
subscaleb
subscale
Urquhart Law (2002)
30
general anxiety
WBQ
0.58
0.54
Lane (1988)
31
trait anxiety
STPI
-0.02
0.54
Vukovic (2010)
77
general anxiety
CBCL Anxiety
-0.24
0.42
ns
0.63
ns
0.63
ns
0.58
ns
0.58
ns
0.63
subscale
Di Battista (2009)
Gonder-Frederick
76
39
(2006)
Howe (2011)
23
social anxiety
fear of hypoglycaemia
SAS-A
state anxiety
trait anxiety
fear of hypoglycaemia
needle anxiety
HFS-C
HFS
STAIC
FACES-PRS
VAS
Liakopoulou (1992)
40
manifest anxiety
CMAS
Rutter-Graham
interview
Simonds (1981)
52
manifest anxiety
CMAS
Note. ns = nonsignificant. PH-CSCS = Piers-Harris Children’s Self-Concept Scale; STAI = Spielberg Trait Anxiety
Inventory; STAIC = Spielberg Trait Anxiety Inventory for Children; FSSC-R = Fear Survey Schedule for Children;
HSCL-25 = Hopkins Symptom Checklist-25; BASC = Behavior Assessment System for Children; WBQ = WellBeing Questionnaire; CBCL = Child Behavior Checklist; HFS-C = Hypoglycemia Fear Survey for Children; STPI =
State-Trait Personality Inventory; SAS-A = Social Anxiety Scale for Adolescents; HFS = Hypoglycemia Fear
Survey; FACES-PRS = Faces Pain Rating Scale; VAS = Visual Analogue Scale; CMAS = Children’s Manifest Anxiety
Scale.
Table 4
Results of regression studies
Study
N
Assessment Type of
anxiety
method
Herzer (2010)
276
STAI(C)
state anxiety
Regression results
β coefficients
Quality
Description
-
When state anxiety scores were added to their regression model HbA 1c values
seemed to be associated to higher state anxiety
trait anxiety
Herzer (2011)
147
STAIC
state anxiety
0.21
150
STAIC
state anxiety:
0.07
La Greca (1995)
Lernmark (1999)
147
42
62
STAI
STAI(-R)
FSSC-R
Higher STAI scores were significant predictors in a multiple regression model. A
State anxiety and trait anxiety were entered into the second block of a multiple
state anxiety:
0.12
trait anxiety:
-0.08
trait anxiety
0.42;
Different regression analyses show that anxiety is not significantly related to
0.11
metabolic control. Boys and girls did differ in anxiety levels
-
A linear regression analysis did not show fear to significantly predict metabolic
general fear
0.61
0.70
14-point increase in anxiety scores is associated with a 1% increase in Hba 1c.
trait anxiety
Ingerski (2010)
In a regression analysis containing family conflict, anxiety and HbA1c values,
anxiety accounted for 20% of the relationship between family conflict and HbA 1c
trait anxiety
Hilliard (2011)
0.78
0.78
regression analysis with HbA1c as dependent variable. Both scores were not
significant predictors of HbA1c
0.65
0.74
control when added together with adaptation and gender
Skinner (2001)
54
WBQ anxiety
general anxiety
-
76
SCQ
interview
0.70
control was added the correlation effect of anxiety no longer exists
subscale
Thernlund (1996)
Gender, anxiety and dietary control were added to the regression. When dietary
Injection anxiety
-
A logistic regression with HbA1c as dependent variable indicated that children
0.78
with injection anxiety and less generalized distress have better metabolic control
Note. STAI = Spielberg Trait Anxiety Inventory; STAIC = Spielberg Trait Anxiety Inventory for Children; STAI-R = Spielberg Trait Anxiety Inventory Revised; FSSC-R =
Fear Survey Schedule for Children; WBQ = Well-Being Questionnaire; SCQ = The Staff Crisis Questionnaire.
Figure Legend
Figure 1. Flow diagram of included studies.
Figure 2. Distribution of total scores attained on the quality checklist.
Figure 3. Proportion (%) of studies meeting the individual qualitative criteria.
Figure 1.
Articles retrieved from
electronic databases
N = 3561
Duplicates removed
N = 736
Excluded articles by
title and abstract
N = 2607
Articles found in
reference lists
N = 14
Full-text articles
reviewed
N = 171*
Excluded articles
after full review
N = 151
Included articles
N = 20
* 61 articles could not be retrieved, even after contacting the authors.
Figure 2.
5
Frequency of scores
4
Quality Score
3
2
1
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Total possible quality score
Figure 3.
Clear objectives
Clear method
Patient characteristics described
Confounders given
Main findings described
Estimates of random variability provided
Losses to follow-up reported
Actual p-values reported
Participating subjects representative
Subjects prepared to participate representative
Staff & facilities representative
Clinical interview & blood test used for verification
Analyses adjusted for different lengths
Statistical test appropriate
Participants recruited from same population
Participants recruited in same time period
Confounders adjusted for
Losses to follow-up adjusted for
Sample large enough for a sign. prevalence estimate
Score=1
Score=2
0
20
40
60
80
Proportion (%) of studies meeting criteria
100
Appendix A. Checklist for measuring study quality
Reporting
Yes
No
1.
Is the hypothesis/aim/objective of the study clearly
described?
1
0
2.
Is the method of assessing anxiety and HbA1c clearly
described in the Introduction or Methods section?
1
0
Are the characteristics of the patients included in the
study clearly described?
1
0
Yes
P*
No
2
1
0
Yes
No
1
0
1
0
1
0
1
0
3.
4.
Are the distributions of principal confounders in each
group of subjects to be compared clearly described?
5.
Are the main findings of the study clearly described?
6.
Does the study provide estimates of or adequate
information to estimate the random variability in the
prevalence/incidence rate?
7.
Have the characteristics of patients lost to follow-up
been described?
8.
Have actual probability values been reported (e.g., 0.035
rather than <0.05) for the main outcomes except where
the probability value is less than 0.001?
Total Reporting Score: ___________
External Validity
9.
Were the subjects asked to participate in the study
representative of the entire population from which they
were recruited?
10. Were those subjects who were prepared to participate
representative of the entire population from which they
were recruited?
11. Were the staff, places, and facilities where the patients
were treated representative of the treatment the
majority of patients receive?
Yes
No
U/D**
1
0
0
1
0
0
1
0
0
Total External Validity Score: ___________
* P = Partially
**U/D = Unable to Determine
Internal Validity - Bias
12. Was the anxiety diagnosis or absence thereof verified
through clinical interview for all study subjects and
was the HbA1c level measured with a blood test?
Yes
(both)
Yes
(1 out of 2)
No
U/D
2
1
0
0
Yes
No
U/D
1
0
0
1
0
0
13. In trials and cohort studies, do the analyses adjust for
different lengths of follow-up of patients, or in casecontrol studies, is the time period over which mood is
assessed the same for cases and control?
14. Were the statistical tests used to assess the main
outcomes appropriate?
Total Bias Score: ___________
Internal Validity - Confounding
Yes
No
U/D
NA
15. Were the patients in different groups recruited from the
same population?
1
0
0
0
16. Were study subjects in different groups recruited over the
same period of time?
1
0
0
0
17. Was there adequate adjustment for confounding in the
analyses from which the main findings were drawn?
1
0
0
1
0
0
18. Were losses of patients to follow-up taken into account?
Total Confounding Score: ___________
Precision
19. Did the study have sufficient precision to provide a
prevalence estimate where the probability value for the
estimate being greater than zero is less than 5%?
Sample size:
<30
30-250
250-1000
1000+
0
1
2
3
4
Total Precision Score: ___________
Total Quality Score: ___________