Introduction

Type 1 diabetes (T1D) is a common pediatric chronic illness, with approximately 18,000 new cases in the USA and over 128,000 cases worldwide diagnosed annually in youth under the age of 20 years [1, 2]. The recent-onset period may be especially challenging for parents of young school-aged children (i.e., 5–9 years old) because parents typically assume the majority of T1D management tasks, such as monitoring glucose levels and administering insulin [3]. Qualitative data suggest that many parents experience feelings of sadness, fear, anger, guilt, and grief following their child’s T1D diagnosis [4, 5]. Many parents also report feeling overwhelmed while trying to implement their child’s T1D regimen into their daily routines and limiting disruption to their family life [6]. Finally, there is evidence that many parents of children report fear and worry specific to diabetes complications, such as hypoglycemia, during the recent-onset period of T1D [7, 8].

Hypoglycemia (i.e., glucose levels < 70 mg/dL or < 3.9 mmol/L) is a common acute complication for children with T1D regardless of their daily insulin therapy (e.g., intensive or conventional) [9]. Mild hypoglycemia can cause symptoms of nausea, dizziness, and irritability [10,11,12]. However, severe episodes of hypoglycemia can lead to seizures and be potentially life-threatening if left untreated [13,14,15]. Unfortunately, hypoglycemia can be difficult to predict for children because they can be very insulin sensitive and often engage in variable eating and physical activity patterns [16]. Consequently, parents may develop significant fear and anxiety related to their child experiencing hypoglycemia, known in the literature as fear of hypoglycemia (FH) [16, 17]. Indeed, FH is common among parents of young children with T1D [18], and high levels of FH are associated with increased distress and reduced quality of life for parents [19, 20]. Further, due to FH, parents may engage in strategies to prevent or avoid child hypoglycemia that purposefully result in higher child glucose levels, above-target child glycated hemoglobin (HbA1c) levels, and greater child risk for developing long-term diabetes-related complications such as retinopathy, nephropathy, and neuropathy [21,22,23].

In contrast to what is known regarding parental FH and glycemic outcomes in young children [16,17,18], to date, there is little known about these associations in parents and young school-age children with recent-onset T1D. This is an important gap in knowledge as early attainment and maintenance of target HbA1c levels in children with T1D is recommended, and therefore, potential barriers to children achieving target HbA1c levels should be addressed [23]. For instance, if parents experience moderate or high levels of FH early in the recent-onset period, these levels could possibly impact their early T1D management behavior and their child’s early glycemic outcomes. Alternatively, if parents experience low levels of FH at diagnosis that increase across the recent-onset period, then it is possible that any effects on parents’ T1D management behavior and children’s glycemic levels could come later. In either case, a better understanding of the course and impact of parental FH on children’s glycemic outcomes in the recent-onset period could inform clinical management.

The current study used a conceptual framework proposed by Pierce and colleagues [24], which links parent characteristics and their adjustment to their child’s T1D diagnosis to individual child characteristics that may directly affect T1D management and outcomes. For instance, this conceptual model posits how parent FH may associate with children’s T1D management and outcomes by affecting parents’ sleep and coping behaviors and increasing parent uptake of less adaptive T1D management behaviors. In the existing literature, there are mixed results when examining the association between parent FH and child glycemic levels [25,26,27]; though notably, these mixed results may be a product of small sample sizes or because these studies used HbA1c as their only glycemic outcome. HbA1c provides a proxy measure of average glycemic levels over an 8–12-week period but does not provide information specific to daily glycemic changes or variability. Additionally, research in adults with T1D suggests that HbA1c can sometimes underestimate or overestimate mean glucose values [28]. Given that children with T1D may exhibit more glycemic variability than adults, using HbA1c alone may not adequately capture how nuanced daily glycemic patterns in youth associate with parental FH, potentially explaining the small effects observed in previous studies. Self-monitoring of glucose (SMBG) has proven clinical utility and provides more detailed information regarding a child’s daily glucose profile than HbA1c [29]. Moreover, it may be possible to use metrics derived from SMBG (e.g., percent of values outside of target range) to further characterize children’s glycemic patterns and relate these to parent FH.

This study aimed to examine the relationship between parents’ FH and child glycemic levels during the first year of a prospective longitudinal study of children with recent-onset T1D. Specifically, this study tested the hypothesis that increases in parental FH would associate with higher child HbA1c levels, higher child glucose values, more child glycemic variability, and a higher percentage of glucose values outside of a child’s target range in a sample of families of young school-age children with recent-onset T1D.

Methods

Participants

Researchers recruited families of young school-age children recently diagnosed with T1D from two large endocrinology clinics in the Midwest and Rocky Mountain regions of the USA. The current study used data from the first 12 months of a 3-year observational and longitudinal study examining adherence and glycemic levels in children with recent-onset T1D (TACKLE T1D). Data for the larger study were collected between November 2014 and December 2018. Eligibility criteria included families of children who were less than 12 months from their T1D diagnosis, between the ages of 5 and 9 years old, English speaking, and on intensive insulin therapy (e.g., multiple daily injections or continuous subcutaneous insulin infusion). Exclusion criteria included children with a developmental disorder (i.e., autism, cerebral palsy, or intellectual disability), any comorbid chronic condition (e.g., renal disease), a diagnosis of type 2 or monogenic diabetes, or any medication use that could impact children’s glycemia (e.g., systemic steroids). The larger study chose to assess adherence and glycemic levels in children aged 5–9 years with recent-onset T1D because this age group is at risk for above-target glycemic levels in the years following diagnosis [30].

Procedures

The Institutional Review Boards at participating hospitals approved all study procedures before recruitment. A study coordinator approached eligible families during their regularly scheduled diabetes clinic visit or informed families of the study via telephone. After a parent/legal guardian provided informed consent for their child, parents completed baseline study measures on a tablet computer. The same parent completed 6-month and 12-month study measures. All study procedures occurred during families’ routine clinic appointments, and researchers collected all data via a REDCap Survey [31]. After each assessment, parents uploaded 14 days of glucose data from their child’s glucometer for the 2 weeks prior to each study visit. Parents received $30 for completing the surveys and uploading glucose data at each assessment, and each child received a toy valued at $10.

Measures

Demographic and Medical Information

Parents provided family and child demographic data (i.e., child’s date of birth, T1D duration, sex, race/ethnicity, family income, socioeconomic status) and their child’s medical information (e.g., treatment regimen, T1D history, and diabetes-related adverse events) at the baseline assessment. Parents reported any changes to their child’s medical information during subsequent assessments. The study team verified medical data through electronic health record review.

Fear of Hypoglycemia

Parents completed the Hypoglycemia Fear Survey for Parents (HFS-P) [32]. This measure contains a “behavior” scale, which assesses behaviors parents may engage in to keep their child from becoming hypoglycemic (i.e., “Allow blood sugar to be a little high to be on the safe side”) and a “worry” scale, which assesses anxiety regarding the negative consequences of hypoglycemia (i.e., “No one being around to help my child during a reaction”). For each item, parents rated how frequently each statement was true for them on a 5-point Likert scale (e.g., from “never” to “very often”), with higher scores indicating higher FH. The current study used the total, behavior, and worry scale scores in its analysis. The behavior and worry scales can be further broken down into four subscales: maintaining high glucose, avoidance/preventing low glucose, helplessness/worry about low glucose, and worry about negative social consequences [33]. However, a recent factor analysis of the HFS-P suggests that the items on the avoidance/preventing low glucose subscale perform poorly and should not be included in the total or behavior scale score [34]. Therefore, researchers did not include the avoidance/preventing low glucose subscale items for the current study when calculating the total or behavior scale scores. All items on the worry scale score were retained. The current sample evidenced acceptable internal consistency for the total (α = 0.89; 19 items), worry (α = 0.92; 15 items), and modified behavior scores (α = 0.75; 4 items). In contrast, the original HFS-P article reported a Cronbach’s alpha of 0.72 and 0.88 for the behavior and worry scale scores, respectively [32].

Child Glucose

Parents shared glucose data from their child’s glucometer for the 2 weeks before their study visit, and researchers used the glucose value ranges recommended by Bergenstal and colleagues [35] to estimate the percentage of glucose levels that were below the target range (< 70 mg/dl), within the target range (70–180 mg/dl), and above the target range (> 180 mg/dl) for each child. Researchers also estimated average daily glucose (mean) and glycemic variability (using standard deviation [SD]) using children’s 14-day glucometer data.

Child HbA1c

The study used HbA1c as a proxy measure of children’s average glycemic levels. Researchers used a finger stick blood sample at each time point via a validated HbA1c kit [36], which they analyzed in a central laboratory using automated high-performance liquid chromatography (reference range, 4.0–6.0%, Tosoh 2.2., Tosoh Corporation, San Francisco, CA). Internal reliability checks revealed this measure to be reliable (r = 0.98) with fresh venous blood samples.

Data Analysis

This study used IBM SPSS 26 to calculate descriptive statistics to characterize the sample and bivariate correlations to explore associations between parents’ FH ratings and children’s glucose data. To better characterize parent responses on the HFS-P, the researchers assessed which items parents most frequently endorsed as “almost always” or “always,” which are response options indicative of higher FH. Researchers also calculated the minimal clinically important difference (MCID) [37] for the HFS-P to understand the amount of change needed for a parent to perceive a clinically meaningful difference in their FH. To do this, researchers used the standard error of measurement (SEM; i.e., SD(√1-α), a common method for calculating MCIDs based on each scale’s internal consistency.

To complete the primary study analyses, the researchers examined the intra-class correlation coefficients (ICC) of the HFS-P scores over time, assessing inter-individual and intra-individual variability. The researchers chose to first examine intra-individual variability of HFS-P scores to determine the level of state-like fluctuations of the construct. Once the researchers characterized these fluctuations in parents’ HFS-P scores, they then estimated multilevel models in SAS PROC MIXED (SAS version 9.4) to examine the relationship between parent’s FH and children’s HbA1c and daily glucose levels over time (i.e., trait-like fluctuations). Because this study used longitudinal data, the dependent variable in each model could change differently for participants over time. Thus, to determine the best fitting model of time for each dependent variable, the researchers centered time at the first measurement occasion and then sequentially entered it as a linear, random linear, quadratic, and random quadratic predictor in four separate models. Then to determine the best fitting model, researchers compared the − 2 log likelihood (− 2LL) value of each model, with lower values indicating better model fit. Once establishing the best fitting model for time for each dependent variable, the researchers next estimated separate models for each child glucose metric as the dependent variable and included parent HFS-P total, behavior, and worry scores and predictors as independent variables. Each model contained two sources of variability, between-person variance (i.e., the variability of responses across participants) and within-person variance (i.e., the variability of an individual’s responses over time). To model these two sources of variability, independent variables were both grand-mean and person-mean centered. Precisely, the independent variables had the grand-mean subtracted from each observation. Then the grand-mean centered variable was aggregated within each participant to form the between-person variable. Finally, the between-person variable was subtracted from each observation to create the within-person variable. Researchers handled missing data using full information maximum likelihood estimation (FIML) [38] with the assumption that data were missing at random.

Results

Descriptive Statistics

Of the 131 families recruited, 126 completed their baseline assessment (96.2%), 113 completed their 6-month assessment (86.3%), and 117 completed their 12-month assessment (89.3%). At baseline, on average, children were 7.45 ± 1.34 years old and 4.61 ± 3.19 months post-T1D diagnosis. Most parents identified their child as non-Hispanic White (89.3%), and approximately half the sample identified their child as female (52%). The mean child HbA1c at baseline was 7.63 ± 1.37%. Caregivers had a mean age of 36.62 ± 6.40 years, 88.8% were mothers, and 80.8% were married. With respect to children’s T1D management, at the baseline assessment, 15.9% of children used an insulin pump, and 15.9% used a continuous glucose monitor (CGM). At the 6-month assessment, 54.9% of children used an insulin pump, and 46.0% used a CGM. At the 12-month assessment, 62.2% of children used an insulin pump and 58.1% used a CGM. Overall, the sample demographics were representative of the patient populations for both recruiting diabetes clinics. There were no significant differences between the families who did and did not complete their 6-month and 12-month assessment for demographic information, baseline HbA1c, glucose variables, and parent baseline HFS-P ratings. Additional descriptive statistics can be found in Table 1.

Table 1 Baseline descriptive characteristics of study sample (N = 126)
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At each time point, all parents in the sample rated the following HFS-P items as either almost always or always: (1) avoid allowing my child to be alone when his/her sugar is likely to be low; (2) feed my child as soon as I feel or see the first sign of low blood sugar; (3) carry some kind of sugar, drink, or food with me for my child; and (4) check my child’s blood sugar often when he/she plans to be at a long event (i.e., school, party). Parent responses for the other items on the HFS-P were more variable. Prior to conducting additional analyses, the researchers examined items for univariate normality (i.e., skewness values > 3.0 or kurtosis values > 10.0 [39], which revealed that no HFS-P score or glucose-related measure violated assumptions. The researchers list a summary of parents’ mean HFS-P scores for each time point in Table 2 and children’s average glucose metrics in Table 3.

Table 2 HFS-P scores at baseline, 6-month, and 12-month assessments
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Table 3 Glucose outcomes at baseline, 6-month, and 12-month assessments
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The Minimal Clinically Important Difference

The MCID for the HFS-P, total, worry, and behavior scores were 4.22, 3.46, and 1.66, respectively. The HFS-P worry score evidenced an MCID change from the baseline to 6-month assessment, such that parents in the sample perceived a clinically meaningful increase in their HFS-P worry scores during this period. Parents also reported a nearly clinically meaningful increase in their HFS-P total scores between the baseline and 6-month assessments. However, there was no meaningful change in parents’ HFS-P behavior scores between baseline and 6-months, and there were no meaningful changes in parents’ HFS-P total, worry, or behavior scores between the 6- and 12-month assessments.

Intra-class Correlations

The ICC for the HFS-P total score was 0.493, which indicated that 49.3% of the variability in the HFS-P total scores was between-person and 50.7% of the variability was within-person. The ICC for the HFS-P behavior scores was 0.559, which indicated that 55.9% of the variability in the HFS-P behavior scores was between-person and 44.1% of the variability was within-person. The ICC for the HFS-P worry scores was 0.459, which indicated that 45.9% of the variability in the HFS-P worry scores was between-person and 54.1% of the variability was within-person. These ICCs demonstrate that fear of hypoglycemia may function as both a state-like and trait-like construct, suggesting that parents’ HFS-P could change over time.

Multilevel Models

To include appropriate covariates in the multilevel models, the researchers examined which demographic variables correlated with each child glucose metric. Results from bivariate correlations revealed that the child age at T1D diagnosis significantly correlated with average glucose values (r = − 0.25), glycemic variability as measured by the standard deviation (r = − 0.26), percent of glucose values in the target range (r = 0.29), and percent of glucose values above the target range (r = − 0.28). Further, the duration of T1D diagnosis at the baseline visit significantly correlated with average glucose values (r = 0.48), glycemic variability (r = 0.45), percent of glucose values in the target range (r = − 0.54), and percent of glucose values above the target range (r = 0.52). Thus, the researchers included these two variables in each multilevel model as covariates to conduct the primary study analyses. Tables 4, 5, and 6 depicts the full results of all multilevel models that predict glucose-related outcomes, which include time, the between- and within HFS-P total scores, HFS-P behavior scores, and HFS-P worry scores, as well as covariates.

Table 4 Associations of HFS-P total scores predicting glucose metrics in multilevel models (N = 126)
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Table 5 Associations of HFS-P behavior scores predicting glucose metrics in multilevel models (N = 126)
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Table 6 Associations of HFS-P worry scores predicting glucose metrics in multilevel models (N = 126)
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Analyses predicting HbA1crevealed a linear random effect for time, such that increases in HbA1c over time varied across individuals. Neither between- nor within-person HFS-P total scores were significant predictors for HbA1c (Table 4). Evaluation of the HFS-P behavior score as a predictor of HbA1c revealed a significant negative relationship for between-person behavior scores (β = − 0.06, p < 0.05), suggesting that parents with higher behavior scores had children with lower HbA1c values aggregated over time (Table 5). Evaluation of the HFS-P worry score did not yield significant predictors for HbA1c (Table 6). Thus, for HbA1c, only time and between-person HFS-P behavior scores were significant predictors.

Analyses predicting average child BG similarly revealed a random linear effect of time, indicating that increases in average child BG over time varied across families. The duration of child T1D diagnosis was a significant predictor, such that children with a longer duration of T1D diagnosis were more likely to have higher average BG levels across assessments. However, between- and within-person HFS-P total scores (Table 4), behavior scores (Table 5), and worry scores (Table 6) did not significantly predict average child BG across assessments. In addition, analyses predicting glycemic variability revealed a similar pattern of results. The models for glycemic variability also demonstrated a random linear effect of time, indicating that increases in glycemic variability varied across families. Further, the duration of child T1D diagnosis was a significant predictor, such that children with a longer duration of T1D diagnosis were more likely to have increased glycemic variability across assessments. Lastly, between- and within-person HFS-P total scores (Table 4), behavior scores (Table 5), and worry scores (Table 6) did not significantly predict glycemic variability across assessments.

Next, analyses predicting the percent of child glucose values in the target range did not establish a better fitting model of time beyond an empty model. Therefore, researchers included the time variable in the models as a linear predictor. The duration of child T1D diagnosis was a significant predictor, such that children who had a longer duration of T1D diagnosis were less likely to have glucose values in the target range across assessments. Neither between- nor within-person HFS-P total scores were significant predictors for the percent of child glucose values in the target range (Table 4). However, the model with parent HFS-P behavior score as a predictor of percent of child glucose values in the target range revealed a significant positive relationship for between-person behavior scores (β = 0.95, p < 0.05), suggesting that parents who reported higher HFS-P behavior scores had children with glucose values that were more likely to fall within the target range across the assessments (Table 5). Further, the model containing the HFS-P worry score as a predictor yielded a significant negative relationship for within-person HFS-P worry scores (β = − 0.27, p < 0.05), suggesting that when parents reported higher than their average HFS-P worry scores, their children demonstrated a lower percentage of glucose values in the target range (Table 6). Thus, for percent of child glucose values in the target range, the duration of child T1D diagnosis, between-person behavior scores, and within-person worry scores were significant predictors.

Analyses predicting the percent of child glucose values below the target range revealed a non-significant random linear effect of time. Neither time, covariates, nor between- or within-person HFS-P Total scores (Table 4), behavior scores (Table 5), or worry scores (Table 6) significantly predicted child glucose values below the target range. In the final set of analyses, the percent of child glucose values above the target range did not establish a better fitting model of time beyond an empty model. Therefore, researchers included the time variable in the models as a linear predictor. The duration of child T1D diagnosis was a significant predictor, such that children with a longer duration of T1D diagnosis were more likely to have glucose values above the target range across assessments. Neither between- nor within-person HFS-P total scores were significant predictors for percent of child glucose values above the target range (Table 4). The model with parents’ HFS-P behavior score as a predictor of percent of child glucose values above the target range revealed a significant negative relationship for within-person behavior scores (β = − 1.15, p < 0.05), suggesting that when parents reported lower than their average HFS-P behavior scores, their children demonstrated a higher percentage of glucose values above the target range (Table 5). Further, the model with parents’ HFS-P worry score as a predictor yielded a significant positive relationship for within-person HFS-P worry scores (β = 0.30, p < 0.05), suggesting when parents reported higher than their average HFS-P worry scores, their children demonstrated a higher percentage of glucose values above the target range (Table 6). Thus, for percent of child glucose values above the target range, the duration of child T1D diagnosis, between-person behavior scores, and within-person worry scores were significant predictors.

Discussion

This study examined trajectories in parental FH over a 12-month period and explored associations between parent FH and child glycemic outcomes in a sample of families of 5 to 9-year-olds with recent-onset T1D. While previous studies demonstrate that FH is common in parents of children with T1D who are greater than 12 months post-diagnosis [18], this study adds to the literature by examining parental FH in a sample of children in the recent-onset period. Notably in this sample, researchers found that parents reported a moderate level of FH at baseline that increased slightly, and to a clinically meaningful degree, for the HFS-P total and worry scores from baseline to the 6-month follow-up. However, contrary to anticipated patterns, parents’ FH scores remained steady from the 6-month to 12-month assessments. Therefore, it appears that parents may report an initial and moderate level of FH near the time of their child’s T1D diagnosis, perhaps because they received diabetes education about the potential negative consequences of hypoglycemia. However, over time and potentially because of repeated pairings of fear with thoughts related to hypoglycemia or pairings of fear with actual episodes of hypoglycemia, parents may develop a conditioned fear response for hypoglycemia which they maintain [40, 41]. Though of interest, children in this sample demonstrated very low levels of hypoglycemia (M = 4.5–6%) across time points, thereby suggesting the perceived risk of hypoglycemia may be sufficient for parents to develop a conditioned fear response.

In partial support of the study hypothesis, variations in parents’ HFS-P total scores over time were unrelated to variations in child glycemic metrics. However, researchers did find that when parents reported higher than their average level of HFS-P worry, their children’s percent of glucose values were less often in the target range and more often in the above-target range, suggesting that in the recent-onset period, when parents experience higher than their usual hypoglycemic-specific worries, their children are more likely to have above-target glucose levels. One possible explanation for this within-person variability in parents’ worry score could be a change in what parents perceive as safe child glucose levels across the recent-onset period. Namely, if parents develop an early conditioned fear response for hypoglycemia, it is possible across the recent-onset period, this response could generalize to include child glucose values > 70 mg/dL (i.e., levels within the target range and above) and in turn, parents’ unintended use of compensatory T1D management behaviors that result in higher child glucose levels.

While the results of this study did not demonstrate a clinically meaningful change in the HFS-P behavior score across the baseline, 6-month, and 12-month assessments, researchers did find that parents who reported higher behavior scores relative to other parents had children with lower HbA1c levels and children who were more likely to have within target glucose levels across assessments. Moreover, researchers found that when parents reported lower than their average HFS-P behavior score, their children demonstrated a higher percentage of glucose values above the target range. Overall, these associations may support the previous hypothesis that some parents over-generalize their hypoglycemia fear response and engage in compensatory T1D management behaviors for child glucose levels within the target range. Alternatively, it is possible a third, unmeasured, variable could explain the associations between parents’ HFS-P behavior scores and children’s glucose levels. Indeed, one study in families of youth with established T1D suggests parenting stress as a mechanism that might link parent FH to higher child glycemic levels [26]. Therefore, to further explore these associations in families of youth with recent-onset T1D, it may be helpful to consider alternative variables and pathways that might connect parent FH to children’s glycemic levels. Likewise, because diabetes management technology has rapidly advanced in the last decade and now offers several tools to assist families with glycemic management (viz., CGM, insulin pumps, hybrid closed loop systems), behavioral medicine needs research to understand how these devices relate to parent FH. Though some early evidence suggests significantly higher parent FH in both insulin pump and CGM users than non-users [34, 42], these studies used a cross-sectional design. In the future, it would be important to examine how new diabetes devices relate to parent FH over time to explore any trait- or state-like differences.

With respect to clinical implications, the researchers would assert that the results of this study and particularly the associations found between parents FH and children’s early glycemic levels support a need for specific education and psychosocial treatment early in the recent-onset period of T1D. Indeed, because parents evidenced a clinically meaningful increase in their HFS-P worry scores between baseline and 6-month, one specific recommendation may be for diabetes providers to start assessing for parental FH soon after their child’s T1D diagnosis and to monitor parental FH at subsequent follow-up appointments up to 12–18 months post-diagnosis. Previous research suggests that parents may experience adverse effects to their mental health (e.g., parental FH and T1D distress), quality of life, and sleep quality/duration following a T1D diagnosis [27, 43, 44]. Current recommendations indicate screening for psychosocial concerns not only in youth diagnosed with T1D but also in their caregivers [23], and the results of the current study suggest FH may be particularly important to monitor in parents across the recent-onset period.

Another specific recommendation from this study may be the addition of adjunctive services (e.g., cognitive behavioral therapy strategies, T1D education) to help parents manage common worries about child hypoglycemia during the recent-onset period, particularly due to the positive associations between parent HFS-P worry scores and child glycemic levels across assessments. Qualitative research shows that parents desire increased support and information related to T1D management, especially in the first few months following their child’s diagnosis [45]. Moreover, there is preliminary evidence supporting the efficacy of a novel telehealth intervention to reduce parent FH [46, 47]. Thus, it is possible implementing a similar intervention to treat parental FH in the recent-onset period may also be helpful as parents adjust to their child’s T1D diagnosis.

Lastly, the results of the current study may inform recommendations specific to child glucose management in the recent-onset period. Specifically, it is notable that children in this study demonstrated increasing HbA1c levels over time which could translate into greater risk for future adverse child health outcomes because of “metabolic memory” (i.e., the link between early target glycemic levels and onset of future micro- and macrovascular complications) [48, 49]. Thus, a recommendation may be for clinics to provide T1D education booster sessions to families across the recent-onset period to reinforce T1D management strategies that do not lead to higher child glucose levels. It is also notable that parents with higher HFS-P behavior scores had children with lower HbA1c values aggregated over time. This association expands upon the literature which heretofore has only reported cross-sectional associations between parent FH and child HbA1c [17]. It reinforces the recommendation that clinics assess for parent FH in the recent-onset period, particularly among parents of children with lower HbA1c levels. It also may underscore the recommendation for T1D education booster sessions across the recent-onset period to reduce parents’ use of hypoglycemia avoidance behaviors that may ultimately lead to higher child glycemic levels.

Limitations

Findings from this study should be considered in the context of a few limitations. First, the study involved mostly mothers indicating a need for future research to examine FH in other caregivers (e.g., father) during the recent-onset period of T1D. Similarly, the racial and ethnic homogeneity of the sample potentially reduces generalizability, calling for a need to replicate the results in a more racially and ethnically diverse sample. Second, the reliance on glucose data downloaded from children’s glucometers could present as a limitation because these data only provide snapshots of glucose concentrations and may not capture the full picture of daily trends and patterns in child glycemia. In the larger study from which the researchers extracted the current data, children were not required to use personal CGM, and the study did not loan children a CGM to use. This is because during the recent-onset period of T1D, personal CGM use varies greatly and may not be the standard of care for all families of youth with T1D. Although CGM could provide a more robust measure of children’s glycemic patterns, the researchers would assert that the current associations discovered between children’s glucometer data and parents’ FH have value both in informing current clinical care and generating new hypotheses. Nonetheless, they acknowledge a need to confirm the study results in a future project that uses CGM to measure children’s glycemic patterns. Third, because of the focus on 14 days of glucose data, one cannot rule out the possibility of white coat adherence leading to lower glucose patterns before each study assessment [50]. To overcome this potential limitation, it might be helpful to consider collecting a longer duration of glucose data from children in future studies. Fourth, due to the number of models conducted in the results, the alpha level of 0.05 could lead to increased risk of type 1 error. The researchers suggest appropriate caution when interpreting the results. Finally, the researchers acknowledge the potential impact the honeymoon period may have on children’s glucose levels irrespective of parental FH. In T1D, the honeymoon period marks a period of reduced exogenous insulin requirements following a T1D diagnosis because of the child maintaining some level of residual beta cell function [51]. As children lose residual beta cell function, their exogenous insulin requirements may increase and further lead to increases in children’s HbA1c, mean daily glucose, and percent of glucose values above the target [52, 53]. Unfortunately, children with T1D may exit the honeymoon period at varying times during the recent-onset period, and there is some evidence suggesting that younger children never have a honeymoon period. Because of this variability, it would be very hard to control for the potential impact of the honeymoon period in any model of child glycemia in the recent-onset period, and so the researchers fully acknowledge this potential confounder of the study results.

Conclusions

The results of this study offer two main findings that add to the literature and can inform the clinical management of school-age children with recent-onset T1D. First, researchers found clinically meaningful increases in parental FH early in the recent-onset period of T1D. Second, researchers are the first to report longitudinal associations between parent FH and child glycemic levels, including child HbA1c, in the recent-onset period of T1D. Specifically, researchers believe the between- and within-person associations for parents’ HFS-P worry and behavior scores, and children’s glycemic levels may support an initial hypothesis that some parents in the recent-onset period over-generalize their hypoglycemia worry and use of hypoglycemia avoidance behaviors to child glucose values within the target range. Thus, as a consequence of these two main findings, diabetes providers should consider adding an assessment of parental FH soon after a child’s T1D diagnosis and monitoring parental FH at subsequent follow-up appointments through the recent-onset period of T1D. Related, if parents’ evidence high levels of hypoglycemia avoidance behaviors or worries about child hypoglycemia, diabetes providers should consider referring parents for additional T1D educational and/or counseling to help them with managing their thoughts, feelings, and behaviors related to child hypoglycemia during the recent-onset period.