Journal of Pediatric Psychology, Vol. 28, No. 3, 2003, pp. 203-211
© 2003 Society of Pediatric Psychology
Blood Glucose Estimations in Adolescents With Type 1 Diabetes: Predictors of Accuracy and Error
University of Florida
All correspondence should be sent to Suzanne Bennett Johnson, Florida State University College of Medicine, Tallahassee, Florida 32306-4300. E-mail: suzanne.johnson{at}med.fsu.edu. Anne Kazak, PhD, ABPP, former Editor, served as accepting editor on this article.
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Abstract |
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Objective To examine predictors of blood glucose (BG) estimation accuracy and errors in adolescents with Type 1 diabetes. Method Seventy-eight adolescents (ages 11-19) rated their physical symptoms and estimated their BG prior to conducting daily BG tests. BG estimation data were subject to an error grid analysis. Hierarchical regression was used to identify predictors of BG estimation accuracy and errors. Results The average participant made accurate BG estimations 37% of the time and clinically relevant BG estimation errors 24% of the time. Girls and older adolescents had higher BG estimation accuracy rates and lower BG estimation error rates than boys and younger adolescents. Higher BG variability was also associated with increased BG estimation errors. Conclusions Although boys, younger adolescents, and those with higher BG variability showed higher BG estimation error rates, most participants showed low rates of BG estimation accuracy and high rates of BG estimation error. Health providers may underestimate the frequency of inappropriate self-treatment as a result of patient difficulty in estimating BG accurately.
Key words: Type 1 diabetes; BG estimations; hypoglycemia symptoms; hyperglycemia symptoms.
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Introduction |
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TopAbstractIntroductionMethodResultsDiscussionReferences |
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Pancreatic failure to produce insulin underlies Type 1 diabetes, a common endocrine disorder of childhood requiring a complex treatment regimen of multiple daily insulin injections and blood glucose (BG) tests as well as careful attention to the child's diet and exercise (Johnson, 1995
). Although the
goal of treatment is to maintain BG levels in the near normal range of 80-120
mg/dL (Schandry, Leopold, & Vogt,
1996), current methods of insulin administration only approximate
normal pancreatic function. Consequently, BG levels frequently vary outside of
the normal range, resulting in hypo- or hyperglycemia.
Hypoglycemia, or low blood glucose level (
70 mg/dL), occurs when there
is excessive insulin in relationship to available blood glucose. It is often
the result of insufficient carbohydrate intake, unusually high levels of
physical exertion, or an increase in insulin dose. Symptoms of hypoglycemia
may include shaking, increased heart rate, sweating, weakness, irritability,
and hunger (McCrimmon, Gold, Deary,
Kelnar, & Frier, 1995). If left untreated, hypoglycemia can
result in confusion, seizures, coma, and death. Consequently, patients are
taught to be alert to signs and symptoms of hypoglycemia and to take
appropriate action (i.e., ingest a simple sugar followed by a
protein-containing snack).
Hyperglycemia, or high blood glucose level (>180 mg/dL) occurs when
there is excessive blood glucose in relationship to available insulin. It may
occur in times of excessive carbohydrate consumption, sedentary behavior,
missed insulin injections or a reduction in insulin dose, or illness. Symptoms
of hyperglycemia may include extreme thirst, frequent urination, nausea, and
physical fatigue (Eastman, Johnson,
Silverstein, Spillar, & McCallum, 1983). Prolonged
hyperglycemia has been associated with the long-term complications of
diabetes: neuropathy, nephropathy, and cardiovascular disease
(Diabetes Control and Complications Trial
[DCCT] Research Group, 1993). Recommendations published by the
American Diabetes Association (ADA;
1996) suggest that hyperglycemia should be managed by regular
urine ketone testing, increased intake of water, exercise, and an additional
dose of insulin.
ADA recommendations also indicate that daily diabetes management should include regular monitoring of BG levels by home BG testing at least three times a day, using test results to make appropriate diabetes management decisions (insulin dose, diet, and exercise). Should a hypo- or hyperglycemic episode be detected, appropriate action can be initiated. However, since hypo- or hyperglycemic episodes can occur at any time, the recommendation of conducting routine testing at least three times a day may be insufficient to detect all episodes of hypo- or hyperglycemia. If a patient believes he or she is experiencing a hypo- or hyperglycemic episode outside of normal testing, ADA recommendations indicate that patients should perform additional BG tests.
Several factors may result in patients relying on subjective symptoms
rather than BG test results when making treatment decisions. Adolescents are
known for being nonadherent to BG testing; thus, they may not conduct the
additional recommended tests. In our clinical experience, many youngsters
insist they can "tell" when their BG is low or high, making
treatment decisions based solely on their intuition. However, if patients are
unaware of their personal physical symptoms that may indicate a hypo- or
hyperglycemic episode, they may not do extra tests, preventing appropriate
treatment. The recognition of symptoms is a complex process that involves
multiple biological and psychological processes, including an internal
physiological reaction (e.g., CNS dysfunction), a physical consequence to the
physiological reaction (e.g., palpitations, trembling), symptom detection, and
accurate interpretation of the symptom
(Cox, Gonder-Frederick, Antoun, Cryer,
& Clarke, 1993).
Diabetes education programs generally teach patients a set of symptoms
related to hypoglycemia and a different set of symptoms associated with
hyperglycemia. However, most patients have unique symptoms that predict hypo-
or hyperglycemia for the individual patient
(Freund, Johnson, Rosenbloom, Alexander,
& Hansen, 1986; Nurick
& Johnson, 1991). Unfortunately, most patients are unaware of
their unique predictive symptoms (Freund
et al., 1986), although several programs have demonstrated that
teaching patients about their own unique predictive symptoms can improve BG
estimation accuracy (Cox et al.,
1989; Cox et al.,
2001; Nurick & Johnson).
Because estimates based on subjective symptoms may lead to self-treatment
decisions (e.g., ingest a carbohydrate snack, increase insulin dose), the
accuracy of these estimations can become critical
(Cox et al., 1993;
Gonder-Frederick, Snyder, & Clarke,
1991). For example, if a patient correctly estimates a low BG
level, the patient would appropriately consume carbohydrates to raise it.
However, if a patient estimates a low BG, but actually has a high BG,
carbohydrate consumption would be inappropriate, resulting in increased
hyperglycemia. Similarly, if a patient inaccurately estimates a low BG level
in the normal range, the patient would fail to take appropriate action (e.g.,
conducting a blood glucose test to confirm hypoglycemia, ingesting a
fast-acting carbohydrate), and a severe hypoglycemic episode may ensue.
The available literature suggests that patients have differing success in
accurately estimate their own BG levels
(Cox et al., 1985;
Eastman et al., 1983,
Freund et al., 1986;
Nurick & Johnson, 1991;
Ruggiero, Kairys, Fritz, & Wood,
1991). Adolescents are generally less accurate than adults (Cox et
al.), and children are generally less accurate than adolescents (Eastman et
al.; Freund et al.; Nurick & Johnson; Ruggiero et al.), although BG
estimation accuracy in children has not been extensively studied. Gender may
also be related to BG estimation accuracy, with adult men better BG estimators
than women (Cox et al.), and adolescent girls more accurate BG estimators than
adolescent boys (Freund et al.).
Several approaches have been used to examine patients' BG estimation
accuracy. Freund et al. (1986)
calculated both the correlation between the patient's actual and estimated BG
level and the percentage of estimates within ±20% of the actual BG
value. However, neither of these approaches provides information about the
clinical impact of an estimation error on the patient's clinical decision
making. Cox et al. (1985)
addressed this issue by developing error grid analysis (EGA), which takes into
account the absolute deviation of estimated BG from actual BG, as well as its
clinical impact.
The EGA measures the clinical significance of BG estimation accuracy and
describes the type of estimation error made
(Cox et al., 1985;
Ruggiero et al., 1991). The
EGA program categorizes BG estimations into five accuracy zones by plotting
the estimates against a reference value
(Figure 1). Zone A represents
clinically accurate estimations that would lead to appropriate self-treatment.
Zone B represents estimation errors that would result in benign self-treatment
(no treatment or no detrimental effect if treatment occurred). Zones C, D, and
E represent clinically relevant errors. Specifically, Zone C includes errors
that lead to overcorrection of clinically acceptable BG levels. Zone D
includes high or low BG levels that were mislabled as normal, resulting in
missed opportunities for corrective action. Zone E includes dangerous errors
such as interpreting hypoglycemia as hyperglycemia or vice versa, resulting in
erroneous self-treatment decisions exactly the opposite of the needed
treatment.
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and lower zones are indicated with 
.
Although a patient's ability to accurately detect hypoglycemia has long
been considered important, few studies have examined potential predictors of
BG estimation accuracy. This issue has become increasingly salient in light of
the results and recommendations of the Diabetes Control and Complications
Trial (DCCT Research Group,
1993). This multisite study used intensive therapy (3-4 insulin
injections and 3-4 BG tests a day with daily insulin adjustment) to reduce BG
levels and delay or prevent the significant complications of diabetes
(retinopathy, neuropathy, nephropathy). The DCCT Research Group
(1994) concluded that all
patients 
13 years of age should be treated with intensive therapy.
However, intensive therapy was also associated with an increased risk of
hypoglycemia, with greatest risk in adolescents
(DCCT Research Group,
1991).
Consequently, it is extremely important to identify those patients with poor BG estimation skills before initiating intensive therapy to reduce the risk of hypoglycemia. Once identified, these predictors can be used by pediatric psychologists to identify patients who are poor estimators of their BG levels, which may interfere with appropriate diabetes management decisions. The purpose of this study was to examine predictors of BG estimation accuracy and errors in a sample (N = 78) of 11- to 19-year-old patients with Type 1 diabetes.
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Method |
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Participants
Adolescents (ages 11-19 years) with Type 1 diabetes (duration >1 year) were recruited from diabetes specialty clinics at two different sites (University of Florida Health Science Center, Gainesville, Florida, n = 45, and Nemours Children's Clinic, Orlando, Florida, n = 33) to participate in trial of intensive therapy; 58% were female and 83.3% were Caucasian. Recruitment occurred at two different sites to assure adequate numbers of study patients across all socioeconomic groups (i.e., patients at the University of Florida Clinic come from lower socioeconomic groups than patients at the Nemours Clinic). Adolescent assent and parental consent to participate were obtained in accordance with the requirements of institutional review boards at both sites.
The procedures described here were part of the trial's educational
component, designed to teach the patient the necessary skills to engage in
intensive therapy safely. All participants in the intensive therapy trial were
part of the study described here. However, only 54% of adolescents approached
to participate in the trial agreed to do so. The primary reasons for refusal
involved the increased demands associated with intensive therapy: increased
clinic visits, increased insulin injections, increased BG testing, and
transportation difficulties getting to the clinic. Consenters and refusers did
not differ in demographic characteristics (age, gender, race), disease status
(disease duration, current glycosylated hemoglobin levels), family
composition, or socioeconomic status
(Tercyak, Johnson, Kirkpatrick, &
Silverstein, 1998) (see Table
I for a summary of participant characteristics).
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Measures
Demographic information (age, gender, disease duration, and maternal
education) was obtained during a regularly scheduled clinic visit.
Symptom Rating Checklist (SRC). The SRC
(Freund et al., 1986) consists
of 27 symptoms of hypoglycemia and hyperglycemia, with symptoms organized by
body part (e.g. symptoms associated with the head, stomach, etc.).
Participants rate on a scale of 0 (not at all) to 6 (a lot) the extent to
which they are experiencing each symptom prior to testing their BG level. In
addition, a space is provided for the patient's estimate of current BG level
and for the actual BG reading, which was obtained using a ONE TOUCH®
Profile® BG testing meter. Participants were taught by the study nurse how
to rate their symptoms and estimate their BG levels before testing and then to
record their actual BG level. Training sessions lasted approximately 15-30
minutes and occurred shortly after recruitment. Before each clinic visit, the
study nurse reminded participants by telephone or e-mail to bring their SRCs
to clinic.
SRC data were used to identify each patient's unique symptom(s) of low and
high BG levels. Participant's ratings from each individual symptom were
correlated with the adolescent's actual BG readings. Correlations of .30
(absolute value) were used to identify the patient's individual symptom(s) of
low or high BG levels. A negative correlation suggested a relationship between
the occurrence of a particular symptom (more frequent) and low BG levels. A
positive correlation indicated a relationship between the occurrence (more
frequent) of a particular symptom and high BG levels.
EGA. As seen in Figure
1, the EGA categorizes BG estimations into five accuracy zones by
plotting BG estimates against reference values
(Cox et al., 1985). Zone A
represents accurate estimations, defined as estimations that deviate 20%
from actual BG or estimations in the hypoglycemic range when actual BG is in
the hypoglycemic range. Zone A estimations would lead to appropriate
self-treatment. Zone B represents estimation errors >20% from actual BG
that result in benign self-treatment. Zones C, D, and E represent clinically
relevant errors. Zone C includes errors that would lead to overcorrecting
clinically acceptable BG levels, resulting in actual BG dropping below 70
mg/dL or rising above 180 mg/dL. Zone D includes high (>180 mg/dL) or low
(<70 mg/dL) BG levels that are mislabeled as normal, thus receiving no
treatment. Zone E reflects extremely dangerous estimations that would lead the
patient to treat symptoms exactly opposite to the actual treatment needed
(i.e., lowering BG when the patient is actually hypoglycemic). Upper zones
represent overestimations; lower zones represent underestimations.
Glycemic Control. Glycemic control was measured by glycosylated hemoglobin (HbAlc) assay conducted at the Shands Core Laboratory using high performance liquid chromatography (reference range: 4.5%-6.1%). In addition, mean BG level and BG variability (SD) was obtained from the patient's ONE TOUCH® Profile® BG testing meter.
Procedure. All participants agreed to begin a trial of intensive therapy for the management of their diabetes. Each patient received a ONE TOUCH® Profile® BG testing meter and was instructed in its use. This meter stores in memory the date, time, and BG test result; these data can be downloaded in the physician's office.
Because intensive therapy has been associated with increased risk of
hypoglycemia (DCCT Research Group,
1991), all participants were asked to use the BG testing meter to
conduct four BG tests a day and to complete an SRC prior to each test. At the
end of a 2-month period, the patient returned to the clinic, the BG testing
meter was downloaded, and the SRCs were collected. Although patients were
asked to complete 120 BG tests and SRCs over this 2-month period, the
frequency of BG testing and SRC completion varied from 25 to 341, with a mean
of 169 BG tests. Despite the variability in the number of BG tests completed
by each participant, no relationship was found between the number of tests and
BG estimation accuracy or BG estimation error. Consequently, all subjects were
included in our analyses.
Each patient's SRC data were used to identify that patient's unique symptoms of low or high BG levels and to identify those individuals who were poor BG estimators and, thus, at risk for inappropriate management of hypoor hyperglycemic episodes. The data downloaded from the BG testing meters were used to calculate each patient's mean BG level, BG variability (SD), and number of BG tests conducted during the 2-month study period.
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Results |
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Symptoms of Low and High BG Levels
To identify individual symptoms of low and high BG levels, each participant's SRC ratings were correlated with actual BG readings, with correlations of
.30 (absolute value) considered significant (positive
correlations indicated symptoms of high BG and negative correlations indicated
symptoms of low BG levels). Subjects with significant correlations were
identified as having a specific symptom of either high or low BG levels. In
general, results indicated that 53.8% of the participants had one or more
symptoms predictive of low BG levels, while 33.3% of the adolescents had one
or more symptoms predictive of high BG levels (see
Table II). Across patients, the
most common low BG symptom was shaky. However, this symptom was
related to low BG levels for less than half (39.7%) of the sample. Other
common symptoms associated with low BG levels were tired (a symptom
for 14.7% of the sample) and hungry (a symptom for 10.3% of the
sample). The most common symptom related to high BG levels was going to
the bathroom a lot; however, this symptom was associated with high BG
levels for only 16.7% of the sample. Other common symptoms related to high BG
were thirsty (12.8%) and headache (10.3%).
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Error Grid Analysis
The EGA was computed for each adolescent. The average participant made
accurate BG estimations (Zone A) 37% of the time and clinically significant
errors (Zones C + D + E) 24% of the time (see
Table III for complete EGA
results).
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Predictors of Accuracy and Error
Simply estimating one's BG levels and self-monitoring symptoms could lead
to improved BG estimation accuracy over time. To test this hypothesis, we
examined the data for participants completing over 100 BG estimates and SRCs,
splitting their data to see if estimations improved over time. There was no
evidence that BG estimation accuracy improved over time. Consequently, all
data provided by each participant were included in the analyses.
Hierarchical regression analyses were used to identify the best predictors of BG estimation accuracy (percentage of estimations in Zone A) and error (percentage of estimations in Zones C + D + E). For each analysis, sets of variables were entered in steps. This approach allowed us to examine the relationship between different types of demographic/disease variables and BG estimation accuracy and errors. The first step in the analysis included demographic variables (age, gender, duration of disease, race, and maternal education). The second step consisted of the glycemic control variables (number of BG tests conducted in the 2-month study period, HbAlc, mean BG level downloaded from the patient's meter, and BG variability, defined as the standard deviation of the meter's BG readings). The third step included information relevant to each participant's hypo- or hyperglycemic symptoms (number of hypo- and number of hyperglycemic symptoms). At each step, only significant predictor variables were retained before progressing to the next step. We report here the final, best prediction models.
The child's gender and age were the only significant predictors of BG
estimation accuracy (percent of estimations in Zone A; see
Table IV), F(2, 75) =
9.3, p < .01. Results indicate that average BG estimation accuracy
was 40% for girls compared to 33% for boys. To explore the age effect, we used
a median split to divide the group into younger (<14 years) and older
adolescents (14 years). Average BG estimation accuracy was 41% for older
adolescents compared to 33% for younger adolescents.
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The best model predicting BG estimation errors (Zones C + D + E) included
gender, age, and BG variability, F(3, 73) = 7.6, p < .01
(Table IV). BG estimation
errors averaged 27% for boys compared to 22% for girls. Younger (<14 years)
adolescents made more BG estimation errors (27%) than older (>14 years)
adolescents (21%). To explore the BG variability effect, we used a median
split to divide the sample into low BG variability (SD < 96) and
high variability (SD 96). Among those adolescents with high BG
variability, estimation errors occurred an average of 27% of the time, whereas
those with low BG variability had an average estimation error rate of 20%.
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Discussion |
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TopAbstractIntroductionMethodResultsDiscussionReferences |
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This study examined predictors of BG estimation accuracy and errors in adolescents with Type 1 diabetes who agreed to participate in a trial of intensive therapy. Because intensive therapy has been associated with increased hypoglycemia, particularly in adolescents (DCCT Research Group, 1991
),
being able to accurately estimate BG levels is critical for patients' safety.
However, previous studies have demonstrated that patients are frequently very
poor BG estimators, leading to errors in diabetes treatment
(Cox et al., 1985;
Eastman et al., 1983;
Freund et al., 1986;
Ruggiero et al., 1991). The
EGA program provides a way to examine BG estimation accuracy and error,
providing valuable information to clinicians who work with this
population.
Our results indicate that adolescents are only moderately accurate at
estimating their BG levels, making accurate estimations (Zone A) an average of
37% of the time and incorrect estimations (Zones C + D + E) an average of 24%
of the time. Ruggiero et al.
(1991) used the EGA to examine
BG estimation accuracy in a sample of 70 11- to 15-year-old campers with Type
1 diabetes and reported similar results: accurate estimations (Zone A)
averaged 34% and errors (Zones C + D + E) averaged 28%. Gonder-Frederick et
al. (1991) reported an average
accuracy estimation rate of 36% for parents and 29% for young children (6-11
years of age); the average estimation error rate was 25% for parents and 35%
for the children. Nurick and Johnson
(1991) reported a 32% average
estimation accuracy rate in a small sample of eight young adult outpatients,
and Cox et al. (1985,
1989) reported average accuracy
rates of 41% and 43% in their adult samples. Taken together, these studies
suggest that clinically significant treatment errors may be more common in
children and adolescents than is generally recognized, may be more common in
children and adolescents than in adults with Type 1 diabetes, and should be
given greater attention by the child's treating physician
(Cox et al., 1989).
In this study, patient characteristics were found to predict BG estimation
accuracy. Girls and older adolescents made a larger percentage of accurate BG
estimations than boys and younger adolescents. Because girls develop
physically and cognitively earlier than boys, they may be more aware of
physiological changes caused by fluctuating BG levels. Freund et al.
(1986) also reported better
estimation accuracy for girls than boys but only for one of four measures
examined; these investigators did not use the EGA to examine their data.
Ruggiero et al. (1991)
reported no gender differences in accurate estimation rates calculated using
the EGA. Ruggerio et al.'s participants were campers, rather than patients in
a diabetes clinic, and their analyses were based on far fewer BG estimations
per camper (28 compared to a mean of 169 in this study). However, Ruggiero et
al. did report a trend for better estimation accuracy rates in older than in
younger children. Gonder-Frederick et al.'s
(1991) reported average
estimation accuracy rate of 29% in 6- to 11-year- old children also supports
the age-related findings reported here. Older adolescents are more cognitively
mature and may be better able to consider the multiple factors (i.e. diet,
exercise) that contribute to BG levels.
Gender, age, and BG variability all predicted errors in BG estimates, with
boys, younger adolescents, and those with greater BG variability making more
BG estimation errors. The high average error rate (35%) reported by
Gonder-Frederick et al. (1991)
for 6- to 11-year-old children is supportive of a developmental interpretation
of the data. Gonder-Frederick et al. also noted that BG variability was
associated with BG estimation accuracy; lower variability was associated with
greater accuracy. Although BG variability per se is rarely the focus of a
child's clinic visit, these data suggest that high variability may interfere
with a child's ability to accurately estimate BG levels and make it more
difficult for the child to make appropriate treatment decisions.
In this study, duration of disease, race, maternal education,
HbA1c, mean BG levels downloaded from the child's BG testing meter,
and the number of BG tests conducted over a 2-month interval failed to predict
BG estimation accuracy or errors. The number of symptoms predictive of high or
low BG levels for each adolescent also did not predict BG estimation accuracy.
However, Freund et al. (1986)
previously documented that adolescents are generally unaware of their unique
symptom-BG relationships. Indeed, one of the components of successful
interventions designed to improve BG estimation accuracy consists of
identifying each patient's unique symptom-BG relationships and teaching the
patient about those symptoms as part of the intervention procedure (Cox,
Gonder-Frederick, Julian, & Clark, 1994; Cox et al.,
1989,
2001; Nurick & Frederick,
1991). Consequently, in the absence of appropriate patient education, it
should come as no surprise that the number of each individual's symptoms
predictive of high or low BG levels would fail to predict BG estimation
accuracy.
Our sample was larger than that in previous studies and included a broad
age range of adolescents with Type 1 diabetes. However, because the patients
conducted their BG estimations and tests at home, participants could have
produced more favorable data by testing their BG prior to recording their
estimation. Although we cannot be certain that the participants in our study
actually estimated their BG levels before conducting their BG tests, this
problem has been addressed in previous studies, which reported that adults and
children rarely biased their data (Cox et
al., 1985; Gonder-Frederick et
al., 1991). In our study, patients' BG estimations and BG readings
were carefully examined for evidence of biased reporting by two methods.
First, we visually examined each patient's data looking for patterns in
reported BG estimates (e.g., estimates were in close proximity to actual BG).
We also consulted with the study nurse at each site regarding the patient's
data validity based on their clinical knowledge of the study participant. We
found no evidence of bias, and all patients' data were retained in the
analyses. In addition, if estimates had been made after the actual readings
were conducted, we would expect to see relatively good BG estimation accuracy.
In fact, our results showed relatively low BG estimation accuracy rates and
relatively high BG estimation error rates. Nevertheless, BG estimation error
rates may be even higher than those reported here.
The results of this study have significant clinical implications. First, patients need to be able to identify their own symptoms of low and high BG levels in order to recognize the need for additional BG testing and the need to make appropriate treatment decisions. The optimal self-management of diabetes includes the frequent monitoring of BG levels, using BG testing to make treatment decisions. However, because hypo- or hyperglycemic episodes can occur at any time, routine BG testing will not detect all such episodes. In addition, although current clinical practice guidelines indicate that additional BG monitoring should occur when patients suspect they are hypo- or hyperglycemic, patients must be able to identify their unique symptoms in order to know when to conduct these additional BG tests. As adolescents are known for nonadherence to BG testing (i.e., they do not have the proper equipment with them, social pressures, etc.), their reliance on internal symptoms increases the importance of identifying each patient's unique symptoms of low or high BG.
Second, demographic predictors of BG estimation accuracy and error rates
inform clinicians that all adolescents are at risk for high rates of
estimation errors, again emphasizing the importance of teaching individual
symptoms. In this study, boys and younger adolescents showed higher BG
estimation error rates than girls and older adolescents, but very few
adolescents showed high rates of BG estimation accuracy and low rates of BG
estimation error. Only 11 adolescents (14% of the sample) were accurate on
more than 50% of their estimations. These results suggest that treatment
errors may be more common than is generally recognized by clinicians. This
study and previous studies have documented unique symptoms predictive of hypo-
or hyperglycemia for most patients (Freund
et al., 1986; Nurick &
Johnson, 1991). Further, educational programs designed to identify
and teach patients about their unique hypo- or hyperglycemic symptoms can
improve BG estimation accuracy (Cox et al.,
1994,
2001; Nurick & Johnson).
Educational programs of this type need to be more widely applied as part of
standard care.
Third, variability in BG levels should also receive increased attention in the clinical practice setting. In this study, high BG variability was associated with increased BG estimation errors, increasing the risk of potentially dangerous self-treatment errors. BG meters are commonly downloaded in the physician's office at the time of routine clinic visits. An examination of BG variability, in addition to mean BG levels, might alert the clinician to those patients at increased risk for BG estimation errors and failures to detect significant hypo- or hyperglycemic episodes.
The results of this study suggest that BG estimation errors are widespread
in adolescent populations. Although the ADA recommends that patients confirm
suspected episodes of hypo- or hyperglycemia by conducting a BG test, if
patients are unable to accurately detect such episodes, they cannot take
appropriate action. In addition to increasing the frequency of BG monitoring,
available programs designed to improve BG estimation accuracy (Cox et al.,
1989,
2001;
Nurick & Johnson, 1991)
need to become part of standard care.
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Acknowledgments |
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This study was supported by National Institutes of Health Grant R01 HD13820. ONE TOUCH® Profile® blood glucose testing meters were provided by Lifescan. We are grateful to the children and their families who participated in this research.
Received July 16, 2001; revision received November 30, 2001; accepted July 3, 2002
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