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Journal of Pediatric Psychology logoLink to Journal of Pediatric Psychology
. 2022 Jun 3;47(9):1081–1089. doi: 10.1093/jpepsy/jsac049

Demographic and Glycemic Factors Linked With Diabetes Distress in Teens With Type 1 Diabetes

Hailey Inverso 1, Lauren M LeStourgeon 2, Angie Parmar 3, Isha Bhangui 4, Bailey Hughes 5, Emma Straton 6, Madeleine Alford 7, Randi Streisand 8,9, Sarah S Jaser 10,✉
PMCID: PMC9801711  PMID: 35656859

Abstract

Objective

Diabetes distress (DD) is a negative emotional response related to the burdens of living with type 1 diabetes (T1D) and is linked with diabetes outcomes, such as hemoglobin A1c (A1c). Yet, less is known about how other glycemic indicators, average blood glucose and time in range, relate to DD, and which demographic characteristics are associated with higher DD.

Methods

In total, 369 teens (Mage 15.6 ± 1.4, 51% female, MT1D duration 6.7 ± 3.8 years) screened for DD using The Problem Areas in Diabetes—Teen Version to determine eligibility for an ongoing multi-site behavioral trial. The associations of DD, demographic factors, and glycemic indicators (A1c, average blood glucose, and time in range) were analyzed.

Results

Twenty-nine percent of teens (n = 95) scored above the clinical cutoff (≥44) for DD. Females scored significantly higher on average than males. Black/African American, non-Hispanic youth screened significantly higher compared to youth from other racial/ethnic groups. Higher DD scores were related to higher A1c and average blood glucose, and lower time in range. Logistic regression models revealed that females were significantly more likely to report clinically elevated DD than males, and teens with higher A1c were 1.3 times more likely to report DD. Age and diabetes duration were not significantly associated with clinically elevated DD scores.

Conclusions

Results demonstrated that DD is most prevalent in Black, non-Hispanic and female teens, and DD is associated with higher average blood glucose and lower time in range. Further investigation into these disparities is warranted to promote optimal health outcomes for teens with T1D.

Keywords: adolescents, diabetes, psychosocial functioning, racial/ethnic identity

Introduction

One in 400 youth under 20 are diagnosed with type 1 diabetes (T1D), with adolescence being the most common time of onset (Maahs et al., 2010). Type 1 diabetes requires a complex medical regimen (Kent & Quinn, 2018), including frequent blood glucose monitoring and insulin administration, and maintaining a balanced diet and exercise routine (Ziegler & Neu, 2018). For adolescents with T1D, this intensive regimen co-occurs with a time of transition, independence, and identity formation (Markowitz et al., 2015); meanwhile, hormonal changes brought on by puberty make it more difficult for adolescents to stay in the target blood glucose range (Datye et al., 2019). While advances in technological devices such as continuous glucose monitors and insulin pumps have benefited individuals with T1D in their management (Zimmerman et al., 2019), only a small percentage of adolescents are meeting the recommended glycemic target of hemoglobin A1c (A1c) <7% (American Diabetes Association, 2022; Foster et al., 2019).

As such, many adolescents experience diabetes distress (DD), a negative emotional response characterized by the worries, concerns, and fears associated with T1D management (Fisher et al., 2019). DD has been linked to poor glycemic control, low diabetes-specific quality of life, and increased depressive and anxiety symptoms, among other negative health outcomes (Beverly et al., 2019). Further, DD is more strongly linked with A1c than depressive symptoms or general mood concerns (Fisher et al., 2010; Hilliard et al., 2016; Hong et al., 2021).

Moreover, while general mental health screenings provide valuable information to refer patients for psychosocial services and individualized care (Frank, 2005), DD screening tools may be more germane to the experiences and sources of illness-specific distress (Fisher et al., 2019; Hagger et al., 2016). A recent systematic review analyzed the prevalence of DD reported in 16 studies and concluded that one-third of adolescents with T1D experience DD symptoms, a rate comparable to that of adults with T1D, demonstrating that adolescent DD without intervention may persist into adulthood (Hagger et al., 2016). These findings provide rationale for screening for DD to identify those who would benefit from diabetes-specific targeted interventions, aligning with psychosocial recommendations from the American Diabetes Association and International Society for Pediatric and Adolescent Diabetes encouraging the use of diabetes-specific screening tools (American Diabetes Association, 2020; Delamater et al., 2018).

Diabetes distress is important to identify because it has been broadly linked to health outcomes (Fisher et al., 2010; Hong et al., 2021). Studies have found that teens with high levels of DD had higher A1c values compared to teens with low levels of DD (Powers et al., 2017; Wasserman et al., 2021). Some studies have attributed DD as a likely cause of inconsistent diabetes self-management resulting in higher A1c (Hessler et al., 2017), while other studies have posited that the relationship between DD and glycemic control may be bidirectional (Pallayova & Taheri, 2014). However, other studies have demonstrated a weak or nonsignificant association between DD and A1c (Hagger et al., 2016), underscoring the need to clarify the relationship between these factors.

While rates of DD differ among adolescent cohorts, previous studies have generally reported higher risk of DD in those with worse glycemic control, less social support, and those who have been diagnosed for longer (Hagger et al., 2016). Yet, the demographic factors associated with high DD remain unclear. Existing literature has shown inconsistent links between racial/ethnic identity and DD. One study that observed demographic differences in DD found higher prevalence in teens/young adults of racial and ethnic marginalized groups (Vesco et al., 2018), yet other studies have shown no racial/ethnic differences in DD (but findings may not be generalizable due to homogenized sampling [80–90% White, non-Hispanic] and small sample size [n = 85–303]) (Beverly et al., 2019; Hong et al., 2021). Given that disparities in diabetes-related complications have been observed among marginalized individuals (Kahkoska et al., 2018), and these disparities may contribute to elevated DD symptoms, it is important to clarify which groups are at highest risk for DD. Further, sex differences in DD symptoms have been inconclusive to date; in a systematic review of 14 studies examining sex differences in DD scores conducted in 2016, 3 studies found that females reported higher DD, and 11 reported no sex difference (Hagger et al., 2016). More recently, studies have consistently found that females adolescents were significantly more likely to report high DD (Hong et al., 2021; Iturralde et al., 2019; Vesco et al., 2018). Thus, the current study seeks to clarify demographic factors associated with increased DD in a larger, more diverse sample than previous studies, and provide further insight into potential disparities in DD as a diabetes outcome.

As noted above, the existing literature has demonstrated a correlation between DD and A1c in teens. However, other glycemic indicators such as time in range and average blood glucose have not been adequately addressed. Time in range, defined as the percentage of time that blood glucose stays within target range (70–180 mg/dL), is a metric commonly used in clinical practice and is measured with continuous glucose monitors (Battelino et al., 2019). Frequent glycemic variability, indicated by a low time in range or an average blood glucose out of target range, may lead to increased distress. While A1c measures average blood glucose levels over the past 3 months, time in range provides a better understanding of day-to-day blood glucose management, and more time spent in range lowers the chances of developing diabetes complications (Wright et al., 2020). Despite advances in diabetes technologies and increasing use of continuous glucose monitors and emphasis on time in range in recent years (DeSalvo et al., 2018), the association between time in range and psychosocial functioning has not been widely examined. Thus, examining the association of average blood glucose and time in range with DD may support the identification of adolescents who are at an increased risk for DD.

The current study analyzed DD among a diverse sample of youth with T1D to address the following aims: (1) to evaluate whether DD is related to specific demographic characteristics and (2) to examine the association of glycemic indicators (average blood glucose and time in range) with DD. Based on earlier studies, we hypothesized that Black/African American, non-Hispanic and female adolescents would endorse the highest DD scores. In line with previous literature demonstrating a link between DD and A1c, we also hypothesized that higher average blood glucose and lower time in range would be correlated with higher DD scores. Finally, we examined the interaction of race/ethnicity and sex in relation to DD as an exploratory aim.

Methods

Participants

Participants included 369 teenagers (51% female, Mage = 15.6 ± 1.4 years, 65% White, 21% Black/African American, 2% Hispanic, 1% Asian/Asian American, 3% Multiracial, 1% American Indian/Alaskan Native) diagnosed with T1D for >12 months (MT1D duration = 6.7 ± 3.8 years, MA1c = 8.9 ± 2.1%). Teens were recruited to participate in an ongoing two-site randomized controlled trial examining the efficacy of a positive psychology text message-based intervention for teens with mild to moderate DD (clinicaltrials.gov identifier: NCT03845465) (Jaser et al., 2020). Three hundred sixty-nine out of 842 approached teens agreed to complete a measure of DD as part of a screening tool for the larger intervention study. The current study is a secondary analysis of screening data.

Procedures

The study protocol was approved via single IRB. Teens were initially identified using eligibility criteria by Research Assistants based on electronic medical records. Teens were approached for recruitment if they were between 13 and 17 years of age, had been diagnosed with T1D for >12 months, and were able to read and speak English. Teens were not eligible if they had a serious developmental disability or another life-threatening medical condition. Teens and parents were approached in-person during routine diabetes clinic visits or contacted over the phone before or after a clinic visit, in response to coronavirus disease 2019 (COVID-19) restrictions (Inverso et al., 2021). Families who expressed interest in participating gave consent/assent for the teen to be screened for mild to moderate DD to determine trial eligibility using The Problem Areas in Diabetes—Teen Version (PAID-T) (Weissberg-Benchell & Antisdel-Lomaglio, 2011). For the current analysis, any potential participant who consented to be screened was included.

Measures

The Problem Areas in Diabetes—Teen Version scale is a 14-item instrument that measures the emotional burden of living with diabetes, including items such as “Feeling sad when I think about having and living with diabetes” and “Feeling overwhelmed by my diabetes regimen.” This measure has been validated for use in adolescents ages 12–18. Items are rated on a 6-point Likert scale (1 = Not a Problem, 6 = Serious Problem; α = .95 this sample). Possible scores range from 14 to 84, with scores ≥44 considered clinically significant (Shapiro et al., 2018).

Medical Chart Review and Glycemic Data Collection

Research Assistants from both sites extracted glycemic and demographic data (including sex, race, ethnicity, age, and date of diagnosis) from consenting teens’ medical records. When available, point of care A1cs from in-person visits were used. Changes in routine diabetes care and recruitment efforts emerged as a result of COVID-19 restrictions (see Inverso et al., 2021 for further details about COVID-19 related changes). After the onset of the COVID-19 pandemic, A1c values were gathered from the clinic appointment closest to the screen date if available (<30 days before or after screening date). If participants had telehealth diabetes visits, they were mailed at-home A1c kits (Coremedica TM) (Jones et al., 2010). If neither of these were available, the estimated A1c from continuous glucose monitors data was used (Fabris et al., 2020). Among the 304 participants who had an A1c available for analysis, 76% (n = 230) had a point-of-care A1c, 10% (n = 29) had a mail-in A1c, 5% (n = 16) had an estimated A1c from continuous glucose monitor data, and 10% (n = 29) had an outside laboratory A1c.

Average blood glucose and time in range values were extracted from clinic notes and continuous glucose monitor data sharing portals, including Dexcom and Glooko. Glycemic data were extracted for study analyses (<30 days before or after screening date) utilizing a 14-day data window (Riddlesworth et al., 2018). If discrepancies in glycemic data existed between continuous glucose monitoring sharing portals and electronic medical records, we used data from the sharing portals. Data pulled from the EMR were extracted using the clinic note closest to their screening event date within (<30 days before or after screening date). Average blood glucose was expressed in mg/dL and time in range was expressed in a percentage.

Data Analytic Plan

All analyses were conducted in SPSS version 28. Adolescent demographics, PAID-T scores, and indicators of glycemic control (A1c, average blood glucose, and time in range) were included in analyses if available. Of 369 teens who were screened, 82%, (N = 304) had an A1c available, 75%, (N = 277) had blood glucose data available, and 56% (N = 207) had time in range data available (only time in range data gathered from continuous glucose monitors were used in the analyses). For teens who screened multiple times, the first screening event was used for analyses unless glycemic data were unavailable, in which case, the second screening event was used if more complete glycemic data were available. Adolescent PAID-T scores were moderately positively skewed; therefore, nonparametric tests were used in all analyses. Due to small sample sizes among different racial/ethnic groups, participants were collapsed into three groups for statistical purposes: non-Hispanic White, non-Hispanic Black, Hispanic and/or other racial identities. Pearson’s correlation coefficients were used to examine associations between PAID-T scores, age, diabetes duration, A1c, average blood glucose, and time in range. Bonferroni corrections were used to examine the differences in PAID-T scores among racial/ethnic groups. Mann–Whitney–Wilcoxon tests were used to examine the differences in PAID-T scores among males and females. Chi-squared tests were used to determine the significant differences of clinically elevated and non-clinically elevated PAID-T scores among males and females. Finally, we conducted logistic regression analyses to assess correlates of clinically elevated PAID-T scores. In the first step of each model, we entered sex (male = 1, female = 2), and race/ethnicity (coded as 1 = non-Hispanic White, 2 = non-Hispanic Black, and 3 = Hispanic/Other). In the second step of each model, we added the glycemic indicator (A1c or time in range). Next, we analyzed the models again, adding an interaction term (race/ethnicity×sex) in the second step of the model to explore the association with clinically elevated PAID-T scores. We analyzed a final model, adding CGM use in the second step of the model to assess whether device use explained the differences in DD, after accounting for racial/ethnic differences.

Results

The PAID-T scores ranged from 14 to 84, with a mean score of 35.1 ± 15.6. In our sample, 29% (N = 106) reported a clinically elevated score (≥44). The most highly endorsed item was “Feeling that my friends or family don't understand how difficult living with diabetes can be.”

In post hoc analyses, we found that Black, non-Hispanic teens in our sample were significantly more likely to use a glucometer (as opposed to a CGM) (49%) than White, non-Hispanic teens (20%) or other teens of color (32%), chi-square = 19.43, p < .001.

Diabetes Distress Scores by Demographic Group

Nonparametric tests revealed that Black/African American, non-Hispanic (M = 39.9, SD = 16) teens scored significantly higher on the PAID-T compared to White, non-Hispanic teens (M = 33.4, SD = 15.4) and Hispanic and other racial identity teens (M = 35.7, SD = 15.1) (Table I). Further, female teens reported statistically higher rates of DD than male teens (Table II); female teens were twice as likely to report clinically elevated DD as male teens (Table II).

Table I.

Bonferroni Corrections for Means Comparisons by Race/Ethnicity Groups

I. J. Difference (I–J) Std. error Sig.
White, NH (n = 235) Black, NH −6.26* 2.03 .007
M = 33.3, SD = 15.3 Hispanic or Other −4.18 3.27 .605
Black, NH (n = 78) White, NH 6.26* 2.03 .007
M = 39.6, SD = 16.1 Hispanic or Other 2.08 3.57 1.000
Hispanic or Other (n = 25) White, NH 4.18 3.27 .605
M = 37.5, SD = 15.7 Black, NH −2.08 3.57 1.000
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*

p < .05.

Table II.

Differences in PAID-T Scores by Sex

Sex N Clinical elevations or mean rank Statistic Significance
Clinical elevations by sex Male 182 35 elevated (19%) X 2 = 15.82 p < .001
Female 187 71 elevated (38%)
Mean scores by sex Male 182 Mean rank = 158.21 U = 12142.00 p < .001
Female 187 Mean rank = 211.07
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Note. PAID-T = The Problem Areas in Diabetes—Teen Version.

Of participants who had an A1c value available, the average was 8.9 ± 2.1%. Seventeen percent of teens met the ADA recommended target <7.0% (American Diabetes Association, 2022). This is in line with findings from the T1D exchange (Foster et al., 2019). For teens that had blood glucose data and time in range data available, average blood glucose was 207 ± 61.2 mg/dL and average time in range was 43.2 ± 19.1% (Table III). Black/African American, non-Hispanic (n = 78) teens had significantly higher A1c (10.3 ± 2.1%), higher average blood glucose (230 ± 63.4 mg/dL), and lower time in range (34.1 ± 21.1%) than their White, non-Hispanic (n = 235) and Hispanic and other racial/ethnic identifying (n = 25) adolescents in our sample. Bivariate correlations among PAID-T scores, A1c (n = 304), average blood glucose (n = 277), time in range (n = 207), age, and diabetes duration are reported in Table IV. Higher levels of DD were significantly associated with higher A1c levels, higher average blood glucose levels, and lower levels of time in range (see Table IV). Diabetes duration and age were not significantly associated with PAID-T scores.

Table III.

Descriptive Statistics for Demographic, Independent and Dependent Variables

Participants (N = 369)  
Child age in years (mean [SD])   15.6 [1.4]  
Child sex, male (N [%])   182 [49] 
Child race (N [%])    
 White   238 [65]
 African American/Black  79 [21]
 Asian   3 [1]  
 Biracial/Multicultural 4 [1] 
 Other 12 [3] 
 Not Indicated   33 [9] 
Child ethnicity (N [%])    
 Non-Hispanic   350 [95] 
 Hispanic   9 [2] 
 Not indicated   10 [3] 
Device usea (N [%])
 Continuous glucose monitor 217 [74] 
 Meter  77 [26] 
Clinically elevated PAID-T ≥ 44 (N [%]) 106 [29]  
Diabetes duration in years (mean [SD])  6.7 [3.8] 
HbA1cb , % (mean [SD])   8.9 [2.1]   
Blood glucosec , mg/dL (mean [SD])  207.0 [61.2] 
Time in ranged (mean [SD])   43.2 [19.1]  
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Note. HbA1c=hemoglobin A1c; PAID-T=The Problem Areas in Diabetes—Teen Version.

At time of screening, a75 teens do not have information on device use, b65 teens do not have a recent HbA1c test result, c92 teens do not have a blood glucose reading, d207 participants who use a continuous glucose monitors have time in range data.

Table IV.

Pearson Correlations With Diabetes Distress.

Variables 1 2 3 4 5 6
1. PAID-T  −
2. HbA1c  .25** −
3. Average blood glucose .31** .78** −
4. Time in range −.25** −.69** −.84** −
5. Age .05  .02  −.04  −.10  −
6. Diabetes duration .01  .13* .02  −.02  .22** −
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Note. HbA1c = hemoglobin A1c; PAID-T = The Problem Areas in Diabetes—Teen Version.

*

p < .05; **p < .001.

Demographic and Glycemic Correlates of Diabetes Distress

We conducted logistic regression analyses to examine demographic and glycemic factors associated with clinically elevated PAID-T scores (total score ≥44). In Model 1 (see Table V) the first step included sex and race/ethnicity (non-Hispanic White, non-Hispanic Black, and Hispanic/Other), and A1c was entered in the final step as the glycemic indicator (n = 284, due to missing racial/ethnic or A1c data). Age and diabetes duration were not included in the regression models due to nonsignificant associations with DD in the bivariate correlational analyses. The final model explained 19% of variance in clinically significant DD scores (R2 = .19). In the first step of the model, sex and race/ethnicity emerged as significant correlates of clinically elevated DD. In the final step of the model, sex was still a significant correlate, with female teens being more than 4 times as likely to have clinically elevated PAID-T scores as male teens (p < .001). A1c was also a significant correlate, such that every percentage higher A1c was associated with a 1.25 increased risk for clinically elevated PAID-T scores (p = .001).

Table V.

Demographic and Glycemic Control Indicators of Diabetes Distress

A1c, N = 284
 TIR, N = 187
Adjusted OR  95% CI  p value  Adjusted OR  95% CI  p value 
Step 1, R2=.14 Step 1, R2=.06
 Sex 4.11  [2.32, 7.28]  <.001  2.66 [1.28, 5.50]  .008 
 Race/ethnicity 1.60  [1.05, 2.46]  .031  1.27  [0.74, 2.20]  .390
Step 2, R2=.19 Step 2, R2=.14
 Sex 4.66  [2.57, 8.45]  <.001  2.92  [1.38, 6.21]  .005
 Race/ethnicity  1.38  [0.88, 2.16]  .156  1.09 [0.61, 1.95]  .760
 Glycemic indicator  1.25  [1.09, 1.42]  <.001  0.97 [0.95, 0.99]  .002
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Note. TIR = time in range (70–180 mg/dL). Race/ethnicity coded as non-Hispanic White, non-Hispanic Black, and Hispanic/Other.

Next, to explore the interaction between race and sex as a predictor of clinically significant DD, we analyzed the model again, with the race×sex interaction term in the second step of the model. The interaction term was not significant, (β = .47, p = .245), and therefore not included in Table V. Finally, we explored CGM use as a predictor of DD by adding it to the second step of the model. CGM use was not significant, (β = .92, p = .824) and therefore we did not include it in Table V.

In Model 2 (see Table V), the first step included sex and race/ethnicity, and the final step was time in range (n = 187, as this was limited to teens using CGM). The final model was significant, explaining 14% of variance in clinically significant DD scores (R2 = .14). Sex emerged as a significant correlate in the final step, with female teens being more than 3 times as likely to have clinically elevated PAID-T scores as male teens (p = .005). In addition, time in range emerged as a significant correlate, such that having higher TIR was associated with a lower chance (<1) of having clinically elevated PAID-T scores (p = .002). Next, we analyzed the model again, adding the race×sex interaction term to the second step of the model. The interaction term was not significant (β = .87, p = .874) and therefore not included in Table V.

Discussion

The current study examined DD in teens with T1D to address inconsistent findings in the literature, assess demographic factors associated with higher DD, and examine the relationship of DD to a wider range of glycemic indicators (average blood glucose and time in range in addition to A1c). In our sample, teens who identified as Black/African American, non-Hispanic endorsed higher DD than their counterparts of other racial and ethnic groups. Participants of other marginalized groups (Hispanic teens and teens of other racial/ethnic identities) endorsed the second highest screening averages, with White, non-Hispanic teens reporting the lowest levels of DD. These results, in line with previous literature and supportive of our hypothesis, demonstrate that youth of racial/ethnic marginalized groups experience more DD than White, non-Hispanic teens (Vesco et al., 2018). These findings may reflect that the burdens of diabetes management are exacerbated for teens of marginalized identities, which may be explained by the impact of structural inequities (e.g., racism, historical oppression, public policy, etc.), including ones within diabetes care (e.g., barriers to equitable health care, unequal access to diabetes devices, healthcare policies, etc.), that contribute to increased DD in teens of marginalized groups (Hill-Briggs et al., 2021).

Further, our results indicated that female teens endorsed higher DD scores compared to male teens, supporting recent findings (Hong et al., 2021; Iturralde et al., 2019; Vesco et al., 2018). These findings, within a relatively large and evenly distributed sample, further clarify the high prevalence of DD among female teens, which was not consistently observed in earlier studies, possibly due to sampling limitations. These findings corroborate the idea that female teens may perceive the burdens of diabetes more intensely than their male counterparts and may be at risk for maladaptive psychosocial functioning. Importantly, females have a prevalence of depression about twice as high as males (Hyde & Mezulis, 2020). The same biological, affective, cognitive, and sociocultural factors at play may also place females as a higher risk for clinically significant DD.

Additionally, higher rates of DD were associated with lower time in range and with higher average blood glucose levels. Results support our hypothesis and build on previous literature linking glycemic indicators, typically A1c, with DD (Powers et al., 2017; Wasserman et al., 2021). Our findings underscore the important role that high variance in glycemic levels may play in DD, which may be explained by frustrations with high blood glucose levels and worries about low blood glucose levels. Further, the blood glucose and time in range indicators represent glycemic levels over a 2-week time period as opposed to A1c which represents a 3-month average. Time in range is more sensitive to variance in levels than A1c, and Wright et al. (2020) have called for time in range to become the standard for glycemic control. As such, findings demonstrate that time in range, as a robust indicator of turbulence in glycemic levels, may be associated with diabetes-specific distress in teens.

A strength of the current study is the relatively large, diverse sample compared to previous samples examining DD, where >80–90% of the sample identified as White, non-Hispanic (Hong et al., 2021; Hood et al., 2018; Vesco et al., 2018). The current sample allowed us to examine the differences in DD related to race/ethnicity, especially in Black/African American teens, and may provide greater generalizability than existing literature. Additionally, utilizing time in range is a particular strength of the study, as few studies have examined the relationship between time in range and patient-reported outcomes due to the recent developments in diabetes technologies, including continuous glucose monitors (Ehrmann et al., 2021).

This study is not without limitations. Due to COVID-19, data collection was inconsistent. The available data may reflect a biased sample (e.g., differences across race/ethnicity, site, device use), and this pattern of missing data may impact the generalizability of the results. While time in range is a novel methodology to assess meeting glycemic targets, time in range in the current study was obtained exclusively from teens using continuous glucose monitors. Teens of marginalized identities experience systematic barriers that result in less frequent access to diabetes devices (Lai et al., 2021), Insurance coverage may also play a role in our outcomes, as it may influence access to devices. However, we did not collect data on insurance coverage and recognize that as a limitation of the current study. Further, data about perceived discrimination were not collected and may inform mechanisms that contribute to disparities in DD levels. Moreover, data indicating consistent device use, such as continuous glucose monitor activity and average number glucometer checks per day, were not included in the current analyses. This poses methodological considerations for future research, as data may be skewed as a result of inconsistent use, and blood glucose values may overrepresent blood glucose monitoring during hypo- or hyper-glycemic episodes. Future studies should consider implementing a cutoff of acceptable device usage to ensure data reliability. As previously mentioned, COVID-19 caused disruptions in routine clinic visits, and therefore impacted glycemic data collection. Due to this, we had to use different types of A1c data, which may have introduced variability. Further, Black, non-Hispanic teens were significantly more likely to have missing A1c data than White, non-Hispanic and Hispanic and other teens. Additionally, in a post hoc analysis, we observed differences in rates of missing data by site (more glycemic data were missing from CNH (22%) than VUMC (14%), chi-square = 4.79, p = .029).

Our findings have clinical implications for teens with T1D by identifying individuals who may be at higher risk for experiencing elevated DD symptoms and may benefit from brief psychosocial interventions. Pediatric psychologists and other endocrinology health care professionals should consider implementing routine screenings for DD and integrating mental health care into diabetes clinical care. Future research should investigate feasibility and barriers to utilizing diabetes-specific psychosocial instruments, such as the PAID-T, in routine clinic procedures to screen for diabetes-related distress symptoms. Evidence-based psychosocial interventions exist to address and treat DD (Fisher et al., 2019), and could be implemented to minimize symptoms of DD to promote wellbeing in teens with T1D.

Acknowledgments

The study was pre-registered at clinicaltrials.gov Identifier: NCT03845465; the plan for this secondary analysis was not formally pre-registered, but the analysis plan for the larger study was pre-registered. The report is not under consideration for publication elsewhere. The manuscript has been seen and reviewed by all authors and that all authors have contributed to it in a meaningful way.

Funding

Research reported in this publication was supported by the National Institutes of Diabetes and Digestive and Kidney Diseases, R01DK121316.

Conflicts of interest: None declared.

Contributor Information

Hailey Inverso, Children’s National Hospital, Center for Translational Research, USA.

Lauren M LeStourgeon, Department of Pediatrics, Vanderbilt University Medical Center, USA.

Angie Parmar, Department of Pediatrics, Vanderbilt University Medical Center, USA.

Isha Bhangui, Children’s National Hospital, Center for Translational Research, USA.

Bailey Hughes, Department of Pediatrics, Vanderbilt University Medical Center, USA.

Emma Straton, Children’s National Hospital, Center for Translational Research, USA.

Madeleine Alford, Children’s National Hospital, Center for Translational Research, USA.

Randi Streisand, Children’s National Hospital, Center for Translational Research, USA; The George Washington University School of Medicine, USA.

Sarah S Jaser, Department of Pediatrics, Vanderbilt University Medical Center, USA.

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