Blood Glucose Monitoring Before and After Type 1 Diabetes Clinic Visits

Blood Glucose Monitoring Before and After Type 1 Diabetes Clinic Visits Objective To determine patterns of blood glucose monitoring in children and adolescents with type 1 diabetes (T1D) before and after routine T1D clinic visits. Methods Blood glucose monitoring data were downloaded at four consecutive routine clinic visits from children and adolescents aged 5–18 years. Linear mixed models were used to analyze patterns of blood glucose monitoring in patients who had at least 28 days of data stored in their blood glucose monitors. Results In general, the frequency of blood glucose monitoring decreased across visits, and younger children engaged in more frequent blood glucose monitoring. Blood glucose monitoring increased before the T1D clinic visits in younger children, but not in adolescents. It declined after the visit regardless of age. Conclusions Members of the T1D care team need to consider that a T1D clinic visit may prompt an increase in blood glucose monitoring when making treatment changes and recommendations. Tailored interventions are needed to maintain that higher level of adherence across time. adherence, adolescents, blood glucose monitoring, children, devices, type 1 diabetes, technology The pancreas maintains blood glucose levels in the 60–120 mg/dl range through the release of two hormones, insulin and glucagon, in individuals without type 1 diabetes (T1D). Insulin permits the absorption and use of glucose, which lowers blood glucose levels, whereas glucagon stimulates cells to release glucose, which raises blood glucose levels. In individuals with T1D, the pancreas ceases insulin production and, thus, the foundation of T1D treatment is intensive insulin therapy, which involves the administration of exogenous insulin delivered via multiple daily injections (three to four injections per day of basal and mealtime insulin) or insulin pump. Individual insulin doses are calculated in the context of current blood glucose level and the number of carbohydrates consumed (if any) and engagement in exercise (American Diabetes Association, 2017). Current T1D treatment methods do not perfectly mimic a normal functioning pancreas, so high and low blood glucose levels do occur. Consequently, individuals are provided a recommended range of blood glucose levels (e.g., 70–180 mg/dl) that they are to attempt to maintain (Bergenstal et al., 2013), although an individual’s target range may vary based on the T1D medical provider’s recommendations. Many individuals with T1D have difficulty achieving in-range blood glucose levels because of a variety of factors including onset of puberty and hormone fluctuations, inaccurate carbohydrate counting or insulin dose calculation and timing, overcorrection of low blood glucose levels, unpredictability of energy expenditure through exercise, and psychosocial factors, including nonadherence, depression, fear of hypoglycemia, and family conflict (Datye, Moore, Russell, & Jaser, 2015). Blood glucose monitoring is critical to T1D management because it is necessary for correctly calculating an appropriate insulin dose. In addition, blood glucose monitoring provides critical information to the T1D provider, so that adjustments to the patient’s treatment regimen can be made. Although the American Diabetes Association does not recommend a specific number of blood glucose checks per day, it does recommend frequent checking including before meals and snacks, before bedtime and exercise, when low blood glucose is suspected, and before critical tasks such as driving (American Diabetes Association, 2017). An overwhelming amount of evidence has demonstrated that more frequent blood glucose monitoring is associated with better glycemic control (Hood, Peterson, Rohan, & Drotar, 2009; Kichler, Kaugars, Maglio, & Alemzadeh, 2012; Miller et al., 2013; Ziegler et al., 2011), which in turn reduces the risk of cardiovascular disease, hypertension, kidney disease/failure, and blindness (Diabetes Control and Complications Trial Research Group, 1993, 1995). Although there may be an upper limit to this effect with >10 blood glucose checks per day showing no added benefit (Miller et al., 2013), most patients do not check that often (Hood et al., 2009; Kichler et al., 2012; McDonough, Clements, DeLurgio, & Patton, 2017; Miller et al., 2013; Noser et al., 2017; Ziegler et al., 2011). Diabetes technology is unique because blood glucose monitoring devices and insulin pumps store data allowing for the objective evaluation (vs. self-report) of T1D treatment adherence. An increasing number of studies use objective data downloaded from T1D devices to examine blood glucose monitoring patterns (Rohan et al., 2014; McDonough et al., 2017; Noser et al., 2017). Such data can also be used to assess white coat adherence, defined as the increase in adherence behaviors before an appointment with a health-care provider (Feinstein, 1990). Only three studies have examined white coat adherence in pediatric T1D, and none have examined it in adults. Driscoll and colleagues (2011) demonstrated that blood glucose monitoring frequency increased before an appointment with the T1D care team (across four separate visits) in young children (aged 2–11 years) who were in better glycemic control (Driscoll et al., 2011). In a study examining white coat adherence in children and adolescents who used insulin pumps, Driscoll and colleagues found that the frequency of blood glucose monitoring, carbohydrate inputs, and insulin boluses delivered increased before the T1D clinic visit, but only in younger children and not adolescents (Driscoll et al., 2016) Finally, a third study found that white coat adherence occurred before a study visit for blood glucose monitoring, carbohydrate inputs, and insulin boluses in adolescents using an insulin pump who were randomized to receive intervention to improve insulin pump adherence; those in the control group did not exhibit an increase in adherence before a study visit (Driscoll et al., 2017). These studies used multiple days of data downloaded from blood glucose monitoring or insulin pump devices in their analyses. In clinical practice, device software defaults to downloading the most recent 2 weeks of data—the period of time in which white coat adherence is most likely to occur (Driscoll et al., 2011; Driscoll et al., 2016). Consequently, for those who exhibit white coat adherence, the default download of their data will overestimate the frequency of their usual blood glucose monitoring, carbohydrate inputs, and insulin administration adherence during the 3-month interval between clinic visits. Previous studies of blood glucose monitoring frequency in T1D have focused only on the days preceding the child’s T1D clinic appointment (i.e., white coat adherence). Pediatric studies of white coat adherence in other conditions such as epilepsy and atopic dermatitis have also examined adherence patterns in the days following the clinic appointment (Krejci-Manwaring et al., 2007; Modi, Morita, & Glauser, 2008). For example, Krecji-Manwaring and colleagues (2007) evaluated adherence to topical medication in a sample of children (i.e., ≤12 years) diagnosed with atopic dermatitis. Electronic monitors were used to determine that adherence increased before the appointment, peaked on the day of the appointment, and then decreased in the days following the appointment. Modi and colleagues (2008) also used electronic monitors to examine white coat adherence before and adherence patterns after an epilepsy clinic visit in children with newly diagnosed epilepsy who were ≤12 years old. Of the five clinic visits, seizure medication adherence increased before every visit and declined following two of the clinic visits. The current study replicates the extant T1D literature by examining white coat adherence with respect to blood glucose monitoring before the T1D clinic visit, and it addresses a gap by examining patterns of blood glucose monitoring after the visit. It was hypothesized that blood glucose monitoring frequency would increase before the appointment—particularly in young children—and then decrease at some point after the appointment. Methods Participants Participants included 54 children and adolescents (48% female, M [SD] age = 12.64 [3.50] years; range = 5.63–18.87) with T1D who were part of a larger observational study consisting of a convenience sample aimed at assessing blood glucose monitoring patterns across time. Participants were eligible for this study if they had been diagnosed with T1D for ≥ 1 year (M [SD] T1D duration = 5.45 [3.58] years; range = 0.99–16.44) and had ≥ 28 days of data downloaded from their blood glucose monitor on at least two T1D clinic visits. Consistent with the T1D population generally, 80% identified as Caucasian, 18% African-American, and 2% as biracial. A total of 39% used an insulin pump. Hemoglobin A1c (i.e., glycemic control), representing the average glucose level during the past 2.5–3 months (Blanc, Barnett, Gleason, Dunn, & Soeldner, 1981) was obtained at each clinic visit using a Siemens Healthcare Diagnostics DCA Vantage (reference range 4.2–6.5). Average hemoglobin A1c for the sample was 8.95% [1.50]; range = 6.10–13.10%. Procedure This study was approved by the Florida State University Institutional Review Board with parents providing informed consent and children ≥ 7 years old providing assent. At study inception in 2009, all participants were provided with a blood glucose monitor (or multiple monitors if needed) and the appropriate number of blood glucose strips for the duration of the study. They agreed to use only the monitors provided as part of this study, and if multiple monitors were provided, they were brought to the clinic visit. Data were downloaded from each participant’s blood glucose monitor at four subsequent routine T1D clinic visits. The average number [SD] of days between clinic visits were as follows: Study Inception and Clinic Visit Download 1 = 97.15 [11.73], Clinic Download Visit 1 and 2 = 100.50 [19.17], Clinic Download Visit 2 and 3 = 118.61 [39.38], and Clinic Download Visits 3 and 4 = 109.15 [40.69] suggesting that participants were generally adherent to the recommendations of being seen every 3 months for T1D care. Data Analysis Plan Once data were downloaded from blood glucose monitors, each participant’s data file was cleaned to identify and correct technological errors (e.g., duplication of data, overlap of dates from the previous clinic visit). If there were <28 days of data downloaded, the data for that participant’s visit were not used. In addition, if >7 consecutive days of blood glucose readings were missing from a participant’s monitor (e.g., went to camp and used a different monitor), then data for that participant’s visit were not used. All remaining data were then restricted to the 40 days before and the 40 days after each clinic visit. We selected 40 days as the approximate midpoint between clinic visits. Descriptive statistics including Ms, SDs, and ranges were calculated for demographic, hemoglobin A1c, and blood glucose monitoring variables (e.g., blood glucose levels, blood glucose monitoring readings per day). Linear mixed modeling for repeated measures was used to evaluate the relationship between frequency of blood glucose monitoring per day and the time before and after T1D clinic visits. Advantages of linear mixed modeling include: (1) ability to model individual change across time; (2) different number of observations per participant is allowable; and (3) adjustment for within-participant dependence in designs with repeated measures in which data from the same person are intraindividually related. Two mixed models were conducted—one focused on the 40 days before the clinic visit and a second focused on the 40 days after the clinic visit. In the final analyses, only significant predictors (visit; day; child’s age) were retained and nonsignificant predictors (e.g., T1D duration; child’s sex) were dropped. An age by day interaction was tested to examine whether white coat adherence was more common in younger children versus adolescents. All analyses were conducted using SAS Version 9.2 (Cary, NC, USA). Results Table I provides descriptive statistics for blood glucose monitoring data downloaded at four T1D clinic visits. Mean blood glucose level (mg/dl) and mean number of blood glucose readings per day were calculated for each participant. Table I also depicts the white coat adherence effect occurring before—and after—the clinic visit. Specifically, the average number of blood glucose readings at 40, 20, and 1 day before and after the T1D clinic appointment is provided as well as SDs. Table I. Blood Glucose Monitoring Ms, SDs, and Ns Before and After Four Separate T1D Clinic Visit   Clinic Visit 1   Clinic Visit 2   Clinic Visit 3   Clinic Visit 4   Before  After  Before  After  Before  After  Before  After  Mean blood glucose (mg/dl) reading per child  220.92 ± 59.15  216.39 ± 60.20  224.94 ± 63.0  217.2 ± 63.97  218.41 ± 53.0  212.89 ± 49.03  231.41 ± 53.99  228.01 ± 52.24  N = 53  N = 51  N = 46  N = 46  N = 46  N = 45  N = 33  N = 32  # Blood glucose readings per day per child  4.80 ± 2.84  4.50 ± 2.55  4.59 ± 2.71  4.99 ± 2.71  4.31 ± 2.52  4.27 ± 2.29  4.08 ± 2.91  3.95 ± 2.41  N = 53  N = 51  N = 46  N = 46  N = 46  N = 45  N = 33  N = 32  # Blood glucose readings at Day 40  4.52 ± 3.22  4.22 ± 3.04  4.48 ± 2.83  4.35 ± 2.7  3.96 ± 2.53  4.09 ± 2.74  4.15 ± 3.54  3.69 ± 2.42  # Blood glucose readings at Day 20  4.70 ± 3.12  4.51 ± 3.20  4.67 ± 3.41  5.52 ± 3.79  4.43 ± 2.43  4.13 ± 2.63  4.67 ± 4.30  4.06 ± 2.88  # Blood glucose readings at Day 1  4.74 ± 3.55  4.33 ± 2.10  4.93 ± 3.23  4.91 ± 3.36  4.46 ± 2.93  4.62 ± 3.07  4.24 ± 3.35  4.03 ± 3.01    Clinic Visit 1   Clinic Visit 2   Clinic Visit 3   Clinic Visit 4   Before  After  Before  After  Before  After  Before  After  Mean blood glucose (mg/dl) reading per child  220.92 ± 59.15  216.39 ± 60.20  224.94 ± 63.0  217.2 ± 63.97  218.41 ± 53.0  212.89 ± 49.03  231.41 ± 53.99  228.01 ± 52.24  N = 53  N = 51  N = 46  N = 46  N = 46  N = 45  N = 33  N = 32  # Blood glucose readings per day per child  4.80 ± 2.84  4.50 ± 2.55  4.59 ± 2.71  4.99 ± 2.71  4.31 ± 2.52  4.27 ± 2.29  4.08 ± 2.91  3.95 ± 2.41  N = 53  N = 51  N = 46  N = 46  N = 46  N = 45  N = 33  N = 32  # Blood glucose readings at Day 40  4.52 ± 3.22  4.22 ± 3.04  4.48 ± 2.83  4.35 ± 2.7  3.96 ± 2.53  4.09 ± 2.74  4.15 ± 3.54  3.69 ± 2.42  # Blood glucose readings at Day 20  4.70 ± 3.12  4.51 ± 3.20  4.67 ± 3.41  5.52 ± 3.79  4.43 ± 2.43  4.13 ± 2.63  4.67 ± 4.30  4.06 ± 2.88  # Blood glucose readings at Day 1  4.74 ± 3.55  4.33 ± 2.10  4.93 ± 3.23  4.91 ± 3.36  4.46 ± 2.93  4.62 ± 3.07  4.24 ± 3.35  4.03 ± 3.01  Table I. Blood Glucose Monitoring Ms, SDs, and Ns Before and After Four Separate T1D Clinic Visit   Clinic Visit 1   Clinic Visit 2   Clinic Visit 3   Clinic Visit 4   Before  After  Before  After  Before  After  Before  After  Mean blood glucose (mg/dl) reading per child  220.92 ± 59.15  216.39 ± 60.20  224.94 ± 63.0  217.2 ± 63.97  218.41 ± 53.0  212.89 ± 49.03  231.41 ± 53.99  228.01 ± 52.24  N = 53  N = 51  N = 46  N = 46  N = 46  N = 45  N = 33  N = 32  # Blood glucose readings per day per child  4.80 ± 2.84  4.50 ± 2.55  4.59 ± 2.71  4.99 ± 2.71  4.31 ± 2.52  4.27 ± 2.29  4.08 ± 2.91  3.95 ± 2.41  N = 53  N = 51  N = 46  N = 46  N = 46  N = 45  N = 33  N = 32  # Blood glucose readings at Day 40  4.52 ± 3.22  4.22 ± 3.04  4.48 ± 2.83  4.35 ± 2.7  3.96 ± 2.53  4.09 ± 2.74  4.15 ± 3.54  3.69 ± 2.42  # Blood glucose readings at Day 20  4.70 ± 3.12  4.51 ± 3.20  4.67 ± 3.41  5.52 ± 3.79  4.43 ± 2.43  4.13 ± 2.63  4.67 ± 4.30  4.06 ± 2.88  # Blood glucose readings at Day 1  4.74 ± 3.55  4.33 ± 2.10  4.93 ± 3.23  4.91 ± 3.36  4.46 ± 2.93  4.62 ± 3.07  4.24 ± 3.35  4.03 ± 3.01    Clinic Visit 1   Clinic Visit 2   Clinic Visit 3   Clinic Visit 4   Before  After  Before  After  Before  After  Before  After  Mean blood glucose (mg/dl) reading per child  220.92 ± 59.15  216.39 ± 60.20  224.94 ± 63.0  217.2 ± 63.97  218.41 ± 53.0  212.89 ± 49.03  231.41 ± 53.99  228.01 ± 52.24  N = 53  N = 51  N = 46  N = 46  N = 46  N = 45  N = 33  N = 32  # Blood glucose readings per day per child  4.80 ± 2.84  4.50 ± 2.55  4.59 ± 2.71  4.99 ± 2.71  4.31 ± 2.52  4.27 ± 2.29  4.08 ± 2.91  3.95 ± 2.41  N = 53  N = 51  N = 46  N = 46  N = 46  N = 45  N = 33  N = 32  # Blood glucose readings at Day 40  4.52 ± 3.22  4.22 ± 3.04  4.48 ± 2.83  4.35 ± 2.7  3.96 ± 2.53  4.09 ± 2.74  4.15 ± 3.54  3.69 ± 2.42  # Blood glucose readings at Day 20  4.70 ± 3.12  4.51 ± 3.20  4.67 ± 3.41  5.52 ± 3.79  4.43 ± 2.43  4.13 ± 2.63  4.67 ± 4.30  4.06 ± 2.88  # Blood glucose readings at Day 1  4.74 ± 3.55  4.33 ± 2.10  4.93 ± 3.23  4.91 ± 3.36  4.46 ± 2.93  4.62 ± 3.07  4.24 ± 3.35  4.03 ± 3.01  Linear Mixed Models One linear mixed model evaluated change in blood glucose monitoring frequency during the 40 days before each clinic visit, across multiple T1D clinic visits. The second linear mixed model evaluated change in blood glucose monitoring frequency during the 40 days after each visit, across multiple T1D clinic visits. In both models, whether blood glucose monitoring frequency changed across visits (i.e., Visit) was examined and whether it was associated with the child’s age, as well as other demographic factors. Visit was significant in both models, with the number of blood glucose checks declining across visits. The only demographic factor that was significant was child age; older children checked their blood glucose less often than younger children in both models. Controlling for visit and age, there was a significant Day by Age interaction in the before visit model (see Table II). As suggested by Cohen and colleagues, Figure 1 depicts the Age by Day interaction seen in the 40-day window before the clinic visit for three ages: 6 (minimum age in our sample), 13 (median age), and 19 (maximum age) (Cohen, Cohen, West, & Aiken, 2003). The figure highlights the main effect for age—younger children checked more than older children—but also illustrates the fact that the white coat adherence effect occurred in younger, not older children, before a clinic visit. While a 6-year old showed an estimated increase of 0.6 checks per day during the 40 days before the clinic visit, change across time for the 13-year old and 19-year old was negligible (0.18 checks and 0.16 checks, respectively). Table II. Linear Mixed Models Demonstrating Blood Glucose Monitoring Patterns Before and After a T1D Clinic Visit Variable  Before clinic visit   After clinic visit   Estimate  p-value  Estimate  p-value  Visit 2  −0.16  .01  0.42  <.0001  Visit 3  −0.25  .001  −0.02  .81  Visit 4  −0.62  <.0001  −0.41  <.0001  Visit 1 (Reference group)  0  –  0  –  Child age  −0.34  <.0001  −0.28  .001  Day  0.00  .001  −0.01  <.0001  Age × day  0.001  .01      Variable  Before clinic visit   After clinic visit   Estimate  p-value  Estimate  p-value  Visit 2  −0.16  .01  0.42  <.0001  Visit 3  −0.25  .001  −0.02  .81  Visit 4  −0.62  <.0001  −0.41  <.0001  Visit 1 (Reference group)  0  –  0  –  Child age  −0.34  <.0001  −0.28  .001  Day  0.00  .001  −0.01  <.0001  Age × day  0.001  .01      Table II. Linear Mixed Models Demonstrating Blood Glucose Monitoring Patterns Before and After a T1D Clinic Visit Variable  Before clinic visit   After clinic visit   Estimate  p-value  Estimate  p-value  Visit 2  −0.16  .01  0.42  <.0001  Visit 3  −0.25  .001  −0.02  .81  Visit 4  −0.62  <.0001  −0.41  <.0001  Visit 1 (Reference group)  0  –  0  –  Child age  −0.34  <.0001  −0.28  .001  Day  0.00  .001  −0.01  <.0001  Age × day  0.001  .01      Variable  Before clinic visit   After clinic visit   Estimate  p-value  Estimate  p-value  Visit 2  −0.16  .01  0.42  <.0001  Visit 3  −0.25  .001  −0.02  .81  Visit 4  −0.62  <.0001  −0.41  <.0001  Visit 1 (Reference group)  0  –  0  –  Child age  −0.34  <.0001  −0.28  .001  Day  0.00  .001  −0.01  <.0001  Age × day  0.001  .01      Figure 1. View largeDownload slide Age main effects and interactions with the number of days before clinic visits. Figure 1. View largeDownload slide Age main effects and interactions with the number of days before clinic visits. In the after visit model, there was a significant main effect of the time (day) since the clinic visit. Children of all ages exhibited an estimated decline of 0.4 checks 40 days after the clinic visit (see Table II). Blood glucose monitoring was highest immediately after the T1D clinic visit and declined thereafter. Discussion This study adds to an existing body of literature demonstrating that blood glucose monitoring increases (i.e., white coat adherence) in young children with T1D when objectively measured data are downloaded from T1D devices (Driscoll et al., 2011; Driscoll et al., 2016; Driscoll et al., 2017; Hood et al., 2009; Marker, Noser, Clements, & Patton, 2017; Patton, Driscoll, & Clements, 2017). The increase in blood glucose checking before the T1D clinic appointment may occur because the scheduled appointment with the T1D team serves as a reminder or motivator for better adherence (Modi, Ingerski, Rausch, Glauser, & Drotar, 2012), or it may be reinforcing to parents and their children with T1D to receive praise and encouragement for better adherence. As this effect occurred only in young children, it appears that parents are likely responsible. Adolescents and older patients in this study did not show this effect. Overall, older children checked less often than younger children, replicating a large literature documenting a decline in blood glucose monitoring during the adolescent years (Helgeson, Honcharuk, Becker, Escobar, & Siminerio, 2011; Ziegler et al., 2011). Given that shifts in independence and responsibility for the T1D treatment regimen typically occur in adolescence (Blackwell et al., 2015; Ingerski et al., 2010; Wiebe et al., 2014), these findings highlight once again the need for parents to stay involved in their adolescents’ care if optimal levels of blood glucose monitoring are to be achieved. In this longitudinal study, we also identified a main effect for Visit, with blood glucose checking declining after the first study visit. These findings suggest that participants may respond to study participation with an initial increase in blood glucose checking but over time, they return to their usual level of care. This initial response may reflect an effort to achieve potential benefits (e.g., praise) and to avoid negative consequences and worry and concern in others (Blackwell et al., 2015). Indeed, several studies have shown that children and adults with T1D engage in misreporting adherence behaviors to appear more favorable (Gonder-Frederick, Julian, Cox, Clarke, & Carter, 1988; Mazze, Pasmantier, Murphy, & Shamoon, 1985; Mazze et al., 1984). These findings also suggest that studies that collect data from a single clinic visit may obtain results that overestimate participant adherence compared with a more longitudinal approach. This is the first study to examine blood glucose checking in T1D after a clinic visit. Controlling for participant age and visit, we found a significant decline in the number of blood glucose checks as the number of days increased from the clinic visit. In this study, there was an average decline of 0.4 checks per day during the course of 40 days post-clinic visit. These findings are consistent with prior studies demonstrating a decline in medication adherence post-clinic visits in children with atopic dermatitis and epilepsy (Krejci-Manwaring et al., 2007; Modi et al., 2012). The practical importance of these findings needs to be addressed. We found that during the 40-day window studied, a young child of 6 years old increased their blood glucose checking ∼0.6 checks per day before a clinic visit. Similarly, we found that the sample as a whole decreased their blood glucose monitoring ∼0.4 checks per day after the clinic visit during the 40-day window studied. Is this change—of less than 1 check per day of clinical relevance? Although a change of 0.4–0.6 checks per day seems small, this amounts to 3–4 checks per week. Given the large research literature documenting that more frequent blood glucose checks are associated with better glycemic control (Miller et al., 2013; Rausch et al., 2012; Schwandt et al., 2017; Telo, de Souza, Andrade, & Schaan, 2016), we would argue that these findings are clinically relevant. This is especially true in cases where children do not monitor their blood glucose frequently to begin with. For example, a change in the self-reported frequency of blood glucose monitoring may not be clinically relevant in the case of the rare child who checks >10 times per day (Miller et al., 2013). In contrast, the more common child—who checks 4 times a day or less—cannot afford to reduce the frequency of their blood glucose monitoring by 3–4 checks per week. For these reasons, members of the T1D care team need to take into consideration the changes in blood glucose monitoring frequency that occur before and after a T1D clinic appointment when making treatment changes and recommendations. Current industry software programs (for both blood glucose monitors and insulin pumps, which also store blood glucose readings) do not allow for sophisticated examination of patterns of adherence between clinic appointments. Thus, T1D care providers are not able to address changes in blood glucose monitoring patterns in the time interval between clinic appointments. If individuals with T1D who exhibit a substantial decline in blood glucose monitoring at the midpoint between clinic appointments could be identified, then tailored interventions could be developed to attempt to maintain motivation for better adherence during the entire 3 months between T1D medical appointments. One obvious solution for these patients is to increase the frequency of their T1D appointments. In fact, more frequent T1D appointments are associated with better glycemic control (Kaufman, Halvorson, & Carpenter, 1999), and multiple missed appointments are associated with suboptimal glycemic control and more frequent episodes of diabetic ketoacidosis (Fortin, Pries, & Kwon, 2016; Markowitz, Volkening, & Laffel, 2014). However, a more viable option with less burden may be to schedule contact with T1D medical personnel between clinic visits via telehealth, telephone, or email in which blood glucose monitoring data are uploaded and then accessed by the provider for review, which may lead to better and more stable adherence across time (Ilkowitz, Choi, Rinke, Vandervoot, & Heptulla, 2016). Indeed, many software programs associated with T1D devices are now cloud based allowing access by multiple people including T1D providers. It may be that direct, not automated, communication from a T1D provider (i.e., physicians, nurse practitioners, and physician assistants) at several intervals between appointments could serve as positive reinforcement to maintain stable adherence across time (Datye et al., 2015). This study has a number of strengths that add to the T1D adherence literature. First, it replicates our previous studies documenting white coat adherence before a T1D clinic visit in young children (Driscoll, 2011). Second, the use of downloaded blood glucose monitoring data allowed for the objective examination of blood glucose monitoring (as opposed to self-report), which is the closest approximation to actual behavior that is available. Finally, this study is the first to examine blood glucose monitoring patterns occurring after an appointment in individuals with T1D. However, these results also need to be evaluated in the context of the study’s limitations. First, only blood glucose monitoring data were examined; however, with increasing use of insulin pump therapy, and a recent study showing that white coat adherence before a clinic visit also occurs with regard to the number of carbohydrate inputs and insulin boluses delivered (Driscoll, et al., 2016), future research should explore patterns of carbohydrate inputs and insulin dosing after a T1D appointment and their relationship to glycemic control. Second, we did not assess distribution of responsibility for the T1D regimen in this study, and future research should do so to determine the extent to which parents versus children engage in white coat adherence. Third, we did not assess motivation for white coat adherence or maintaining/increasing blood glucose monitoring after the appointment (i.e., social desirability). Fourth, these findings are relevant only to patients who regularly use meters and bring their meters into their clinic visits. Whether patients who fail to use meters or fail to bring them to their clinic visit exhibit change in blood glucose checking frequency before or after a clinic visit remains unknown. Finally, these results were obtained during active study participation, not from medical record review. Future research in which data are obtained from the electronic medical record as part of routine care (i.e., a retrospective study) would help to determine if white coat adherence occurs outside of the research setting, although there is some evidence that white coat adherence occurs naturally in the clinic environment. However, blood glucose monitoring data obtained from medical records would need to contain >2 weeks of data, which is currently not likely given the standard default settings of most blood glucose monitoring software programs. 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Journal of the American Academy of Dermatology , 56, 211– 216. doi: S0190-9622(06)02523-0 [pii] 10.1016/j.jaad.2006.05.073 Google Scholar CrossRef Search ADS PubMed  Marker A. M., Noser A. E., Clements M. A., Patton S. R. ( 2017). Shared responsibility for type 1 diabetes care is associated with glycemic variability and risk of glycemic excursions in youth. Journal of Pediatric Psychology , in press. [epub ahead of print] doi: 10.1093/jpepsy/jsx08 Markowitz J. T., Volkening L. K., Laffel L. M. ( 2014). Care utilization in a pediatric diabetes clinic: Cancellations, parental attendance, and mental health appointments. Journal of Pediatrics , 164, 1384– 1389. doi: 10.1016/j.jpeds.2014.01.045 Google Scholar CrossRef Search ADS PubMed  Mazze R. S., Pasmantier R., Murphy J. A., Shamoon H. ( 1985). Self-monitoring of capillary blood glucose: Changing the performance of individuals with diabetes. 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W.; DPV Initiative. ( 2017). Longitudinal trajectories of metabolic control from childhood to young adulthood in type 1 diabetes from a large German/Austrian registry: A group-based modeling approach. Diabetes Care , 40, 309– 316. doi: 10.2337/dc16-1625 Google Scholar CrossRef Search ADS PubMed  Telo G. H., de Souza M. S., Andrade T. S., Schaan B. D. ( 2016). Comparison between adherence assessments and blood glucose monitoring measures to predict glycemic control in adults with type 1 diabetes: A cross-sectional study. Diabetology & Metablic Syndrome , 8, 54. doi: 10.1186/s13098-016-0162-4. Google Scholar CrossRef Search ADS   Wiebe D. J., Chow C. M., Palmer D. L., Butner J., Butler J. M., Osborn P., Berg C. A. ( 2014). Developmental processes associated with longitudinal declines in parental responsibility and adherence to type 1 diabetes management across adolescence. Journal of Pediatric Psychology , 39, 532– 541. doi: 10.1093/jpepsy/jsu006 Google Scholar CrossRef Search ADS PubMed  Ziegler R., Heidtmann B., Hilgard D., Hofer S., Rosenbauer J., Holl R. ( 2011). Frequency of SMBG correlates with hba1c and acute complications in children and adolescents with type 1 diabetes. Pediatric Diabetes , 12, 11– 17. doi: 10.1111/j.1399-5448.2010.00650.xPDI650 [pii] Google Scholar CrossRef Search ADS PubMed  © The Author(s) 2017. Published by Oxford University Press on behalf of the Society of Pediatric Psychology. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Pediatric Psychology Oxford University Press

Blood Glucose Monitoring Before and After Type 1 Diabetes Clinic Visits

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Oxford University Press
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© The Author(s) 2017. Published by Oxford University Press on behalf of the Society of Pediatric Psychology. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com
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0146-8693
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1465-735X
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10.1093/jpepsy/jsx151
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Abstract

Objective To determine patterns of blood glucose monitoring in children and adolescents with type 1 diabetes (T1D) before and after routine T1D clinic visits. Methods Blood glucose monitoring data were downloaded at four consecutive routine clinic visits from children and adolescents aged 5–18 years. Linear mixed models were used to analyze patterns of blood glucose monitoring in patients who had at least 28 days of data stored in their blood glucose monitors. Results In general, the frequency of blood glucose monitoring decreased across visits, and younger children engaged in more frequent blood glucose monitoring. Blood glucose monitoring increased before the T1D clinic visits in younger children, but not in adolescents. It declined after the visit regardless of age. Conclusions Members of the T1D care team need to consider that a T1D clinic visit may prompt an increase in blood glucose monitoring when making treatment changes and recommendations. Tailored interventions are needed to maintain that higher level of adherence across time. adherence, adolescents, blood glucose monitoring, children, devices, type 1 diabetes, technology The pancreas maintains blood glucose levels in the 60–120 mg/dl range through the release of two hormones, insulin and glucagon, in individuals without type 1 diabetes (T1D). Insulin permits the absorption and use of glucose, which lowers blood glucose levels, whereas glucagon stimulates cells to release glucose, which raises blood glucose levels. In individuals with T1D, the pancreas ceases insulin production and, thus, the foundation of T1D treatment is intensive insulin therapy, which involves the administration of exogenous insulin delivered via multiple daily injections (three to four injections per day of basal and mealtime insulin) or insulin pump. Individual insulin doses are calculated in the context of current blood glucose level and the number of carbohydrates consumed (if any) and engagement in exercise (American Diabetes Association, 2017). Current T1D treatment methods do not perfectly mimic a normal functioning pancreas, so high and low blood glucose levels do occur. Consequently, individuals are provided a recommended range of blood glucose levels (e.g., 70–180 mg/dl) that they are to attempt to maintain (Bergenstal et al., 2013), although an individual’s target range may vary based on the T1D medical provider’s recommendations. Many individuals with T1D have difficulty achieving in-range blood glucose levels because of a variety of factors including onset of puberty and hormone fluctuations, inaccurate carbohydrate counting or insulin dose calculation and timing, overcorrection of low blood glucose levels, unpredictability of energy expenditure through exercise, and psychosocial factors, including nonadherence, depression, fear of hypoglycemia, and family conflict (Datye, Moore, Russell, & Jaser, 2015). Blood glucose monitoring is critical to T1D management because it is necessary for correctly calculating an appropriate insulin dose. In addition, blood glucose monitoring provides critical information to the T1D provider, so that adjustments to the patient’s treatment regimen can be made. Although the American Diabetes Association does not recommend a specific number of blood glucose checks per day, it does recommend frequent checking including before meals and snacks, before bedtime and exercise, when low blood glucose is suspected, and before critical tasks such as driving (American Diabetes Association, 2017). An overwhelming amount of evidence has demonstrated that more frequent blood glucose monitoring is associated with better glycemic control (Hood, Peterson, Rohan, & Drotar, 2009; Kichler, Kaugars, Maglio, & Alemzadeh, 2012; Miller et al., 2013; Ziegler et al., 2011), which in turn reduces the risk of cardiovascular disease, hypertension, kidney disease/failure, and blindness (Diabetes Control and Complications Trial Research Group, 1993, 1995). Although there may be an upper limit to this effect with >10 blood glucose checks per day showing no added benefit (Miller et al., 2013), most patients do not check that often (Hood et al., 2009; Kichler et al., 2012; McDonough, Clements, DeLurgio, & Patton, 2017; Miller et al., 2013; Noser et al., 2017; Ziegler et al., 2011). Diabetes technology is unique because blood glucose monitoring devices and insulin pumps store data allowing for the objective evaluation (vs. self-report) of T1D treatment adherence. An increasing number of studies use objective data downloaded from T1D devices to examine blood glucose monitoring patterns (Rohan et al., 2014; McDonough et al., 2017; Noser et al., 2017). Such data can also be used to assess white coat adherence, defined as the increase in adherence behaviors before an appointment with a health-care provider (Feinstein, 1990). Only three studies have examined white coat adherence in pediatric T1D, and none have examined it in adults. Driscoll and colleagues (2011) demonstrated that blood glucose monitoring frequency increased before an appointment with the T1D care team (across four separate visits) in young children (aged 2–11 years) who were in better glycemic control (Driscoll et al., 2011). In a study examining white coat adherence in children and adolescents who used insulin pumps, Driscoll and colleagues found that the frequency of blood glucose monitoring, carbohydrate inputs, and insulin boluses delivered increased before the T1D clinic visit, but only in younger children and not adolescents (Driscoll et al., 2016) Finally, a third study found that white coat adherence occurred before a study visit for blood glucose monitoring, carbohydrate inputs, and insulin boluses in adolescents using an insulin pump who were randomized to receive intervention to improve insulin pump adherence; those in the control group did not exhibit an increase in adherence before a study visit (Driscoll et al., 2017). These studies used multiple days of data downloaded from blood glucose monitoring or insulin pump devices in their analyses. In clinical practice, device software defaults to downloading the most recent 2 weeks of data—the period of time in which white coat adherence is most likely to occur (Driscoll et al., 2011; Driscoll et al., 2016). Consequently, for those who exhibit white coat adherence, the default download of their data will overestimate the frequency of their usual blood glucose monitoring, carbohydrate inputs, and insulin administration adherence during the 3-month interval between clinic visits. Previous studies of blood glucose monitoring frequency in T1D have focused only on the days preceding the child’s T1D clinic appointment (i.e., white coat adherence). Pediatric studies of white coat adherence in other conditions such as epilepsy and atopic dermatitis have also examined adherence patterns in the days following the clinic appointment (Krejci-Manwaring et al., 2007; Modi, Morita, & Glauser, 2008). For example, Krecji-Manwaring and colleagues (2007) evaluated adherence to topical medication in a sample of children (i.e., ≤12 years) diagnosed with atopic dermatitis. Electronic monitors were used to determine that adherence increased before the appointment, peaked on the day of the appointment, and then decreased in the days following the appointment. Modi and colleagues (2008) also used electronic monitors to examine white coat adherence before and adherence patterns after an epilepsy clinic visit in children with newly diagnosed epilepsy who were ≤12 years old. Of the five clinic visits, seizure medication adherence increased before every visit and declined following two of the clinic visits. The current study replicates the extant T1D literature by examining white coat adherence with respect to blood glucose monitoring before the T1D clinic visit, and it addresses a gap by examining patterns of blood glucose monitoring after the visit. It was hypothesized that blood glucose monitoring frequency would increase before the appointment—particularly in young children—and then decrease at some point after the appointment. Methods Participants Participants included 54 children and adolescents (48% female, M [SD] age = 12.64 [3.50] years; range = 5.63–18.87) with T1D who were part of a larger observational study consisting of a convenience sample aimed at assessing blood glucose monitoring patterns across time. Participants were eligible for this study if they had been diagnosed with T1D for ≥ 1 year (M [SD] T1D duration = 5.45 [3.58] years; range = 0.99–16.44) and had ≥ 28 days of data downloaded from their blood glucose monitor on at least two T1D clinic visits. Consistent with the T1D population generally, 80% identified as Caucasian, 18% African-American, and 2% as biracial. A total of 39% used an insulin pump. Hemoglobin A1c (i.e., glycemic control), representing the average glucose level during the past 2.5–3 months (Blanc, Barnett, Gleason, Dunn, & Soeldner, 1981) was obtained at each clinic visit using a Siemens Healthcare Diagnostics DCA Vantage (reference range 4.2–6.5). Average hemoglobin A1c for the sample was 8.95% [1.50]; range = 6.10–13.10%. Procedure This study was approved by the Florida State University Institutional Review Board with parents providing informed consent and children ≥ 7 years old providing assent. At study inception in 2009, all participants were provided with a blood glucose monitor (or multiple monitors if needed) and the appropriate number of blood glucose strips for the duration of the study. They agreed to use only the monitors provided as part of this study, and if multiple monitors were provided, they were brought to the clinic visit. Data were downloaded from each participant’s blood glucose monitor at four subsequent routine T1D clinic visits. The average number [SD] of days between clinic visits were as follows: Study Inception and Clinic Visit Download 1 = 97.15 [11.73], Clinic Download Visit 1 and 2 = 100.50 [19.17], Clinic Download Visit 2 and 3 = 118.61 [39.38], and Clinic Download Visits 3 and 4 = 109.15 [40.69] suggesting that participants were generally adherent to the recommendations of being seen every 3 months for T1D care. Data Analysis Plan Once data were downloaded from blood glucose monitors, each participant’s data file was cleaned to identify and correct technological errors (e.g., duplication of data, overlap of dates from the previous clinic visit). If there were <28 days of data downloaded, the data for that participant’s visit were not used. In addition, if >7 consecutive days of blood glucose readings were missing from a participant’s monitor (e.g., went to camp and used a different monitor), then data for that participant’s visit were not used. All remaining data were then restricted to the 40 days before and the 40 days after each clinic visit. We selected 40 days as the approximate midpoint between clinic visits. Descriptive statistics including Ms, SDs, and ranges were calculated for demographic, hemoglobin A1c, and blood glucose monitoring variables (e.g., blood glucose levels, blood glucose monitoring readings per day). Linear mixed modeling for repeated measures was used to evaluate the relationship between frequency of blood glucose monitoring per day and the time before and after T1D clinic visits. Advantages of linear mixed modeling include: (1) ability to model individual change across time; (2) different number of observations per participant is allowable; and (3) adjustment for within-participant dependence in designs with repeated measures in which data from the same person are intraindividually related. Two mixed models were conducted—one focused on the 40 days before the clinic visit and a second focused on the 40 days after the clinic visit. In the final analyses, only significant predictors (visit; day; child’s age) were retained and nonsignificant predictors (e.g., T1D duration; child’s sex) were dropped. An age by day interaction was tested to examine whether white coat adherence was more common in younger children versus adolescents. All analyses were conducted using SAS Version 9.2 (Cary, NC, USA). Results Table I provides descriptive statistics for blood glucose monitoring data downloaded at four T1D clinic visits. Mean blood glucose level (mg/dl) and mean number of blood glucose readings per day were calculated for each participant. Table I also depicts the white coat adherence effect occurring before—and after—the clinic visit. Specifically, the average number of blood glucose readings at 40, 20, and 1 day before and after the T1D clinic appointment is provided as well as SDs. Table I. Blood Glucose Monitoring Ms, SDs, and Ns Before and After Four Separate T1D Clinic Visit   Clinic Visit 1   Clinic Visit 2   Clinic Visit 3   Clinic Visit 4   Before  After  Before  After  Before  After  Before  After  Mean blood glucose (mg/dl) reading per child  220.92 ± 59.15  216.39 ± 60.20  224.94 ± 63.0  217.2 ± 63.97  218.41 ± 53.0  212.89 ± 49.03  231.41 ± 53.99  228.01 ± 52.24  N = 53  N = 51  N = 46  N = 46  N = 46  N = 45  N = 33  N = 32  # Blood glucose readings per day per child  4.80 ± 2.84  4.50 ± 2.55  4.59 ± 2.71  4.99 ± 2.71  4.31 ± 2.52  4.27 ± 2.29  4.08 ± 2.91  3.95 ± 2.41  N = 53  N = 51  N = 46  N = 46  N = 46  N = 45  N = 33  N = 32  # Blood glucose readings at Day 40  4.52 ± 3.22  4.22 ± 3.04  4.48 ± 2.83  4.35 ± 2.7  3.96 ± 2.53  4.09 ± 2.74  4.15 ± 3.54  3.69 ± 2.42  # Blood glucose readings at Day 20  4.70 ± 3.12  4.51 ± 3.20  4.67 ± 3.41  5.52 ± 3.79  4.43 ± 2.43  4.13 ± 2.63  4.67 ± 4.30  4.06 ± 2.88  # Blood glucose readings at Day 1  4.74 ± 3.55  4.33 ± 2.10  4.93 ± 3.23  4.91 ± 3.36  4.46 ± 2.93  4.62 ± 3.07  4.24 ± 3.35  4.03 ± 3.01    Clinic Visit 1   Clinic Visit 2   Clinic Visit 3   Clinic Visit 4   Before  After  Before  After  Before  After  Before  After  Mean blood glucose (mg/dl) reading per child  220.92 ± 59.15  216.39 ± 60.20  224.94 ± 63.0  217.2 ± 63.97  218.41 ± 53.0  212.89 ± 49.03  231.41 ± 53.99  228.01 ± 52.24  N = 53  N = 51  N = 46  N = 46  N = 46  N = 45  N = 33  N = 32  # Blood glucose readings per day per child  4.80 ± 2.84  4.50 ± 2.55  4.59 ± 2.71  4.99 ± 2.71  4.31 ± 2.52  4.27 ± 2.29  4.08 ± 2.91  3.95 ± 2.41  N = 53  N = 51  N = 46  N = 46  N = 46  N = 45  N = 33  N = 32  # Blood glucose readings at Day 40  4.52 ± 3.22  4.22 ± 3.04  4.48 ± 2.83  4.35 ± 2.7  3.96 ± 2.53  4.09 ± 2.74  4.15 ± 3.54  3.69 ± 2.42  # Blood glucose readings at Day 20  4.70 ± 3.12  4.51 ± 3.20  4.67 ± 3.41  5.52 ± 3.79  4.43 ± 2.43  4.13 ± 2.63  4.67 ± 4.30  4.06 ± 2.88  # Blood glucose readings at Day 1  4.74 ± 3.55  4.33 ± 2.10  4.93 ± 3.23  4.91 ± 3.36  4.46 ± 2.93  4.62 ± 3.07  4.24 ± 3.35  4.03 ± 3.01  Table I. Blood Glucose Monitoring Ms, SDs, and Ns Before and After Four Separate T1D Clinic Visit   Clinic Visit 1   Clinic Visit 2   Clinic Visit 3   Clinic Visit 4   Before  After  Before  After  Before  After  Before  After  Mean blood glucose (mg/dl) reading per child  220.92 ± 59.15  216.39 ± 60.20  224.94 ± 63.0  217.2 ± 63.97  218.41 ± 53.0  212.89 ± 49.03  231.41 ± 53.99  228.01 ± 52.24  N = 53  N = 51  N = 46  N = 46  N = 46  N = 45  N = 33  N = 32  # Blood glucose readings per day per child  4.80 ± 2.84  4.50 ± 2.55  4.59 ± 2.71  4.99 ± 2.71  4.31 ± 2.52  4.27 ± 2.29  4.08 ± 2.91  3.95 ± 2.41  N = 53  N = 51  N = 46  N = 46  N = 46  N = 45  N = 33  N = 32  # Blood glucose readings at Day 40  4.52 ± 3.22  4.22 ± 3.04  4.48 ± 2.83  4.35 ± 2.7  3.96 ± 2.53  4.09 ± 2.74  4.15 ± 3.54  3.69 ± 2.42  # Blood glucose readings at Day 20  4.70 ± 3.12  4.51 ± 3.20  4.67 ± 3.41  5.52 ± 3.79  4.43 ± 2.43  4.13 ± 2.63  4.67 ± 4.30  4.06 ± 2.88  # Blood glucose readings at Day 1  4.74 ± 3.55  4.33 ± 2.10  4.93 ± 3.23  4.91 ± 3.36  4.46 ± 2.93  4.62 ± 3.07  4.24 ± 3.35  4.03 ± 3.01    Clinic Visit 1   Clinic Visit 2   Clinic Visit 3   Clinic Visit 4   Before  After  Before  After  Before  After  Before  After  Mean blood glucose (mg/dl) reading per child  220.92 ± 59.15  216.39 ± 60.20  224.94 ± 63.0  217.2 ± 63.97  218.41 ± 53.0  212.89 ± 49.03  231.41 ± 53.99  228.01 ± 52.24  N = 53  N = 51  N = 46  N = 46  N = 46  N = 45  N = 33  N = 32  # Blood glucose readings per day per child  4.80 ± 2.84  4.50 ± 2.55  4.59 ± 2.71  4.99 ± 2.71  4.31 ± 2.52  4.27 ± 2.29  4.08 ± 2.91  3.95 ± 2.41  N = 53  N = 51  N = 46  N = 46  N = 46  N = 45  N = 33  N = 32  # Blood glucose readings at Day 40  4.52 ± 3.22  4.22 ± 3.04  4.48 ± 2.83  4.35 ± 2.7  3.96 ± 2.53  4.09 ± 2.74  4.15 ± 3.54  3.69 ± 2.42  # Blood glucose readings at Day 20  4.70 ± 3.12  4.51 ± 3.20  4.67 ± 3.41  5.52 ± 3.79  4.43 ± 2.43  4.13 ± 2.63  4.67 ± 4.30  4.06 ± 2.88  # Blood glucose readings at Day 1  4.74 ± 3.55  4.33 ± 2.10  4.93 ± 3.23  4.91 ± 3.36  4.46 ± 2.93  4.62 ± 3.07  4.24 ± 3.35  4.03 ± 3.01  Linear Mixed Models One linear mixed model evaluated change in blood glucose monitoring frequency during the 40 days before each clinic visit, across multiple T1D clinic visits. The second linear mixed model evaluated change in blood glucose monitoring frequency during the 40 days after each visit, across multiple T1D clinic visits. In both models, whether blood glucose monitoring frequency changed across visits (i.e., Visit) was examined and whether it was associated with the child’s age, as well as other demographic factors. Visit was significant in both models, with the number of blood glucose checks declining across visits. The only demographic factor that was significant was child age; older children checked their blood glucose less often than younger children in both models. Controlling for visit and age, there was a significant Day by Age interaction in the before visit model (see Table II). As suggested by Cohen and colleagues, Figure 1 depicts the Age by Day interaction seen in the 40-day window before the clinic visit for three ages: 6 (minimum age in our sample), 13 (median age), and 19 (maximum age) (Cohen, Cohen, West, & Aiken, 2003). The figure highlights the main effect for age—younger children checked more than older children—but also illustrates the fact that the white coat adherence effect occurred in younger, not older children, before a clinic visit. While a 6-year old showed an estimated increase of 0.6 checks per day during the 40 days before the clinic visit, change across time for the 13-year old and 19-year old was negligible (0.18 checks and 0.16 checks, respectively). Table II. Linear Mixed Models Demonstrating Blood Glucose Monitoring Patterns Before and After a T1D Clinic Visit Variable  Before clinic visit   After clinic visit   Estimate  p-value  Estimate  p-value  Visit 2  −0.16  .01  0.42  <.0001  Visit 3  −0.25  .001  −0.02  .81  Visit 4  −0.62  <.0001  −0.41  <.0001  Visit 1 (Reference group)  0  –  0  –  Child age  −0.34  <.0001  −0.28  .001  Day  0.00  .001  −0.01  <.0001  Age × day  0.001  .01      Variable  Before clinic visit   After clinic visit   Estimate  p-value  Estimate  p-value  Visit 2  −0.16  .01  0.42  <.0001  Visit 3  −0.25  .001  −0.02  .81  Visit 4  −0.62  <.0001  −0.41  <.0001  Visit 1 (Reference group)  0  –  0  –  Child age  −0.34  <.0001  −0.28  .001  Day  0.00  .001  −0.01  <.0001  Age × day  0.001  .01      Table II. Linear Mixed Models Demonstrating Blood Glucose Monitoring Patterns Before and After a T1D Clinic Visit Variable  Before clinic visit   After clinic visit   Estimate  p-value  Estimate  p-value  Visit 2  −0.16  .01  0.42  <.0001  Visit 3  −0.25  .001  −0.02  .81  Visit 4  −0.62  <.0001  −0.41  <.0001  Visit 1 (Reference group)  0  –  0  –  Child age  −0.34  <.0001  −0.28  .001  Day  0.00  .001  −0.01  <.0001  Age × day  0.001  .01      Variable  Before clinic visit   After clinic visit   Estimate  p-value  Estimate  p-value  Visit 2  −0.16  .01  0.42  <.0001  Visit 3  −0.25  .001  −0.02  .81  Visit 4  −0.62  <.0001  −0.41  <.0001  Visit 1 (Reference group)  0  –  0  –  Child age  −0.34  <.0001  −0.28  .001  Day  0.00  .001  −0.01  <.0001  Age × day  0.001  .01      Figure 1. View largeDownload slide Age main effects and interactions with the number of days before clinic visits. Figure 1. View largeDownload slide Age main effects and interactions with the number of days before clinic visits. In the after visit model, there was a significant main effect of the time (day) since the clinic visit. Children of all ages exhibited an estimated decline of 0.4 checks 40 days after the clinic visit (see Table II). Blood glucose monitoring was highest immediately after the T1D clinic visit and declined thereafter. Discussion This study adds to an existing body of literature demonstrating that blood glucose monitoring increases (i.e., white coat adherence) in young children with T1D when objectively measured data are downloaded from T1D devices (Driscoll et al., 2011; Driscoll et al., 2016; Driscoll et al., 2017; Hood et al., 2009; Marker, Noser, Clements, & Patton, 2017; Patton, Driscoll, & Clements, 2017). The increase in blood glucose checking before the T1D clinic appointment may occur because the scheduled appointment with the T1D team serves as a reminder or motivator for better adherence (Modi, Ingerski, Rausch, Glauser, & Drotar, 2012), or it may be reinforcing to parents and their children with T1D to receive praise and encouragement for better adherence. As this effect occurred only in young children, it appears that parents are likely responsible. Adolescents and older patients in this study did not show this effect. Overall, older children checked less often than younger children, replicating a large literature documenting a decline in blood glucose monitoring during the adolescent years (Helgeson, Honcharuk, Becker, Escobar, & Siminerio, 2011; Ziegler et al., 2011). Given that shifts in independence and responsibility for the T1D treatment regimen typically occur in adolescence (Blackwell et al., 2015; Ingerski et al., 2010; Wiebe et al., 2014), these findings highlight once again the need for parents to stay involved in their adolescents’ care if optimal levels of blood glucose monitoring are to be achieved. In this longitudinal study, we also identified a main effect for Visit, with blood glucose checking declining after the first study visit. These findings suggest that participants may respond to study participation with an initial increase in blood glucose checking but over time, they return to their usual level of care. This initial response may reflect an effort to achieve potential benefits (e.g., praise) and to avoid negative consequences and worry and concern in others (Blackwell et al., 2015). Indeed, several studies have shown that children and adults with T1D engage in misreporting adherence behaviors to appear more favorable (Gonder-Frederick, Julian, Cox, Clarke, & Carter, 1988; Mazze, Pasmantier, Murphy, & Shamoon, 1985; Mazze et al., 1984). These findings also suggest that studies that collect data from a single clinic visit may obtain results that overestimate participant adherence compared with a more longitudinal approach. This is the first study to examine blood glucose checking in T1D after a clinic visit. Controlling for participant age and visit, we found a significant decline in the number of blood glucose checks as the number of days increased from the clinic visit. In this study, there was an average decline of 0.4 checks per day during the course of 40 days post-clinic visit. These findings are consistent with prior studies demonstrating a decline in medication adherence post-clinic visits in children with atopic dermatitis and epilepsy (Krejci-Manwaring et al., 2007; Modi et al., 2012). The practical importance of these findings needs to be addressed. We found that during the 40-day window studied, a young child of 6 years old increased their blood glucose checking ∼0.6 checks per day before a clinic visit. Similarly, we found that the sample as a whole decreased their blood glucose monitoring ∼0.4 checks per day after the clinic visit during the 40-day window studied. Is this change—of less than 1 check per day of clinical relevance? Although a change of 0.4–0.6 checks per day seems small, this amounts to 3–4 checks per week. Given the large research literature documenting that more frequent blood glucose checks are associated with better glycemic control (Miller et al., 2013; Rausch et al., 2012; Schwandt et al., 2017; Telo, de Souza, Andrade, & Schaan, 2016), we would argue that these findings are clinically relevant. This is especially true in cases where children do not monitor their blood glucose frequently to begin with. For example, a change in the self-reported frequency of blood glucose monitoring may not be clinically relevant in the case of the rare child who checks >10 times per day (Miller et al., 2013). In contrast, the more common child—who checks 4 times a day or less—cannot afford to reduce the frequency of their blood glucose monitoring by 3–4 checks per week. For these reasons, members of the T1D care team need to take into consideration the changes in blood glucose monitoring frequency that occur before and after a T1D clinic appointment when making treatment changes and recommendations. Current industry software programs (for both blood glucose monitors and insulin pumps, which also store blood glucose readings) do not allow for sophisticated examination of patterns of adherence between clinic appointments. Thus, T1D care providers are not able to address changes in blood glucose monitoring patterns in the time interval between clinic appointments. If individuals with T1D who exhibit a substantial decline in blood glucose monitoring at the midpoint between clinic appointments could be identified, then tailored interventions could be developed to attempt to maintain motivation for better adherence during the entire 3 months between T1D medical appointments. One obvious solution for these patients is to increase the frequency of their T1D appointments. In fact, more frequent T1D appointments are associated with better glycemic control (Kaufman, Halvorson, & Carpenter, 1999), and multiple missed appointments are associated with suboptimal glycemic control and more frequent episodes of diabetic ketoacidosis (Fortin, Pries, & Kwon, 2016; Markowitz, Volkening, & Laffel, 2014). However, a more viable option with less burden may be to schedule contact with T1D medical personnel between clinic visits via telehealth, telephone, or email in which blood glucose monitoring data are uploaded and then accessed by the provider for review, which may lead to better and more stable adherence across time (Ilkowitz, Choi, Rinke, Vandervoot, & Heptulla, 2016). Indeed, many software programs associated with T1D devices are now cloud based allowing access by multiple people including T1D providers. It may be that direct, not automated, communication from a T1D provider (i.e., physicians, nurse practitioners, and physician assistants) at several intervals between appointments could serve as positive reinforcement to maintain stable adherence across time (Datye et al., 2015). This study has a number of strengths that add to the T1D adherence literature. First, it replicates our previous studies documenting white coat adherence before a T1D clinic visit in young children (Driscoll, 2011). Second, the use of downloaded blood glucose monitoring data allowed for the objective examination of blood glucose monitoring (as opposed to self-report), which is the closest approximation to actual behavior that is available. Finally, this study is the first to examine blood glucose monitoring patterns occurring after an appointment in individuals with T1D. However, these results also need to be evaluated in the context of the study’s limitations. First, only blood glucose monitoring data were examined; however, with increasing use of insulin pump therapy, and a recent study showing that white coat adherence before a clinic visit also occurs with regard to the number of carbohydrate inputs and insulin boluses delivered (Driscoll, et al., 2016), future research should explore patterns of carbohydrate inputs and insulin dosing after a T1D appointment and their relationship to glycemic control. Second, we did not assess distribution of responsibility for the T1D regimen in this study, and future research should do so to determine the extent to which parents versus children engage in white coat adherence. Third, we did not assess motivation for white coat adherence or maintaining/increasing blood glucose monitoring after the appointment (i.e., social desirability). Fourth, these findings are relevant only to patients who regularly use meters and bring their meters into their clinic visits. Whether patients who fail to use meters or fail to bring them to their clinic visit exhibit change in blood glucose checking frequency before or after a clinic visit remains unknown. Finally, these results were obtained during active study participation, not from medical record review. Future research in which data are obtained from the electronic medical record as part of routine care (i.e., a retrospective study) would help to determine if white coat adherence occurs outside of the research setting, although there is some evidence that white coat adherence occurs naturally in the clinic environment. However, blood glucose monitoring data obtained from medical records would need to contain >2 weeks of data, which is currently not likely given the standard default settings of most blood glucose monitoring software programs. 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Journal of Pediatric PsychologyOxford University Press

Published: Dec 23, 2017

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