Abstract
PURPOSE –
Type 1 diabetes (T1D) is one of the most complex and demanding chronic diseases in adolescents. Given the detrimental impact of problems with executive function (EF; the ability to initiate, plan, and monitor behavior) on health outcomes in adolescents with T1D, most studies have examined common diabetes-specific outcomes related to self-management and glycemic control. This study aims to investigate the impact of executive dysfunction on health-related quality of life (HRQoL; an individual’s perceived impact of illness and treatment on daily functioning) in adolescents with T1D from a multi-informant perspective.
METHODS –
In this cross-sectional study, 169 adolescents (mean±SD age 15.9±1.3 years) and their parents reported on adolescent EF and HRQoL (assessed by the BRIEF and PedsQL, respectively). Parent-youth interview and chart review provided demographic and clinical characteristics. Statistical analyses encompassed bivariate correlations, t-tests, chi-squared tests, and multivariable analyses.
RESULTS –
Adolescent self-reports and parent proxy-reports identified 13% and 32% of adolescents, respectively, as having executive dysfunction. Poorer adolescent EF was associated with poorer adolescent HRQoL by both adolescent self-report and parent proxy-report, respectively. In significant multivariable models, adjusted for adolescent age, sex, diabetes duration, and glycemic control, 21% and 24% of the variance in adolescent self-reported and parent proxy-reported HRQoL were explained by adolescent self-reported and parent proxy-reported executive dysfunction. A significant interaction of sex with adolescent self-report of executive dysfunction indicated that executive dysfunction had a greater negative impact on HRQoL in females than males (p<.01).
CONCLUSIONS –
Findings suggest that the impact of EF problems in adolescents with T1D goes beyond diabetes-specific outcomes and focuses attention on the need to evaluate and preserve HRQoL.
INTRODUCTION
Type 1 diabetes (T1D) is one of the most complex and common chronic diseases in youth, affecting more than a million young persons below the age of 20 worldwide [1]. To reduce the risk of short-term complications (e.g., hypoglycemia), long-term adverse health outcomes (e.g., vision loss, kidney failure, cardiovascular complications), and premature death, this chronic illness requires lifelong intensive daily self-management behaviors (i.e., blood glucose monitoring, exogenous insulin delivery, regulation of carbohydrate intake and exercise) [2]. Adolescence is a period of natural separation from the family unit, during which parental involvement in diabetes management decreases and adolescents are expected to accept increasing responsibility for their T1D self-care [3]. During this time, adolescents with T1D are at high risk for self-management difficulties. Indeed, only a minority of adolescents attain glycemic targets [4]. Adolescents struggle to balance the demands of diabetes self-care with the competing challenges of normal adolescent developmental, including the need to integrate their illness into their identity [5, 6]. These challenges are known to potentially create fertile ground for a negative impact on quality of life (QoL) for adolescents with T1D [7] [8]. The World Health Organization (WHO) defines QoL as “an individual’s perception of their position in life in the context of the culture and value systems in which they live and in relation to their goals, expectations, standards and concerns” [9]. By the definition of the ISOQOL Dictionary of Quality of Life and Health Outcomes Measurement [10], QoL includes components of material comfort, health and personal safety, relationships etc., while health-related QoL (HRQoL) more specifically, refers to “the health aspects of QoL, generally considered to reflect the impact of disease and treatment on disability and daily functioning,” and reflects “the impact of perceived health on one’s ability to live a fulfilling life”[10].
During the natural shift towards independent self-care [11], adolescents increasingly need to rely on their own cognitive competencies to plan, organize, and complete diabetes self-management tasks. This set of cognitive abilities is also referred to as executive function (EF). EF is an umbrella term referring to a set of diverse, higher-order cognitive skills that include inhibitory control, cognitive flexibility, sustaining attention, and planning – the cognitive competencies necessary for adequate self-management behaviors. In the past decade, the negative impact of EF problems for diabetes outcomes has been identified in a number of cross-sectional and longitudinal studies [12–14]. However, notwithstanding this research, few studies have investigated the broader impact that EF problems in adolescents with T1D may have on their well-being, including HRQoL. In some other chronic illnesses, problems with EF have been found to be related to poorer HRQoL [15], pointing to the importance of investigating HRQoL as an important person-reported outcome in chronic illness.
Moreover, previous research has often relied on either parent- or adolescent- report of EF. [16]. In addition, it is recognized that parent proxy-report of adolescent HRQoL is often lower than adolescent self-report [17, 18]. Although adolescents may be the best reporters of their internal states and feelings, parents’ perceptions often influence healthcare utilization [19]. Hence, to comprehensively understand adolescent HRQoL and EF, it is imperative to use both self-reports and parent proxy-reports. Thus, given the surge of diabetes research on the impact of EF problems on health outcomes in T1D [7, 12–14], and the recognition of HRQoL as an important person-reported outcome in youth with T1D [20] [21, 22], it is timely to evaluate the potential impact of EF problems on HRQoL in adolescents with T1D.
To fill this gap, we utilized a multi-informant (adolescent and parent) approach to assess adolescent EF and HRQoL, as well as to evaluate associations between EF problems and adolescent HRQoL. We further sought to assess any sexual dimorphisms in these associations.
RESEARCH DESIGN AND METHODS
Study Sample
The study sample was recruited from the pediatric diabetes clinic at the Joslin Diabetes Center (Boston, USA) and included adolescents with the following eligibility criteria: 13 to 17 years old, T1D duration ≥6 months, daily insulin dose ≥0.5 U/kg, HbA1c 6.5–11.0%, and fluency in English. Exclusion criteria included major developmental or cognitive disorder or diagnosed major psychiatric disorder (e.g., diagnosed eating disorder), and other psychosocial, medical, or family issues that would prevent study participation. Eligibility was initially assessed by medical record review and confirmed by parent-teen interview. In this study adolescent EF and HRQoL were assessed in 169 adolescents with T1D; 168 parents also provided proxy-reports of adolescent EF and HRQoL (1 parent had invalid survey data due to too many missing responses and was excluded from analyses). At the study entry, all adolescents and their parents signed informed assent and consent forms, respectively. Study visits occurred in a private room within the clinic on the same day as adolescents’ medical visits. Adolescents and parents received a modest monetary compensation for completing surveys. The study protocol was approved by the Institutional Review Board prior to implementation of any study procedures.
Data Collection and Measures
Adolescent-parent interviews and medical record reviews, conducted by trained research assistants, provided detailed data on diabetes management (e.g., insulin doses, insulin regimen, glucose monitoring) as well as family socio-demographic data. The following measures were completed by adolescents and their parents.
Behavior Rating Inventory of Executive Function
The Behavior Rating Inventory of Executive Function Self-Report and parent proxy-report (BRIEF-SR and BRIEF) [23] assess adolescent EF. The 80-item adolescent self-report and 86-item parent proxy-report questionnaires assess whether adolescents express certain EF-related behaviors on a 3-point Likert scale (never, sometimes, or often scored as 1, 2, and 3, respectively). Higher scores indicate more EF problems. The questionnaire consists of a behavioral regulation index (BRI; the ability to shift cognitive set, inhibit behavioral responses, and control emotions) and a metacognition index (MI; the ability to initiate, plan & organize, monitor behavior, and use working memory). In the current study, we found high internal consistency of both the adolescent self-report of adolescent EF (Cronbach α=.97), and parent proxy-report of adolescent EF (Cronbach α=.98). The BRIEF surveys, completed on paper, were scored according to published recommendations [23]. T-scores in the range of 60–65 are considered mildly elevated, while T-scores >65 are considered clinically elevated. For the purpose of the current study, we used a cut-off score ≥60 on the Global Executive Composite, the Metacognition Index, and/or the Behavioral Regulation Index as indicative of executive dysfunction.
Pediatric Quality of Life Inventory (PedsQL) Generic Core Scales
The 23-item Pediatric Quality of Life Inventory™ (PedsQL) 4.0 Generic Core Scales [24] self-report and parent proxy-report assess adolescents’ general HRQoL. Adolescents and parents completed the PedsQL on tablet computers using REDCap software [25]. The scores use a five-point Likert scale (0 = never a problem, 1 = almost never a problem, 2 = sometimes a problem, 3 = often a problem, and 4 = almost always a problem). Responses are scored as follows: 0 scored as 100, 1 as 75, 2 as 50, 3 as 25, and 4 as 0. The Physical Health scale is composed of 8 items and the Psychosocial Health scale is composed of 15 items. Total PedsQL score results from averaging all items. Physical and Psychosocial scale scores result from averaging the items in the scale. Therefore, total and scale scores range from 0 to 100 (highest HRQoL). In the current study, we found high internal consistency of both the self-report HRQoL (Cronbach α=.93), and parent proxy-report HRQoL (Cronbach α=.93).
Glycemic Control
Each adolescent provided blood for HbA1c assay as part of routine clinical care, uniformly measured by the Roche Cobas Analyzer (reference range 4.0 – 6.0%).
Statistical Analyses
Analyses were conducted in three steps. First, descriptive statistics described the sample and are presented as Mean±SD for continuous data and as percentages for categorical data. Second, we conducted paired samples t-tests and Pearson correlations to compare adolescent self-report to parent proxy-report of adolescent EF and HRQoL. We used correlations to assess associations of EF and HRQOL scores with age and HbA1c and t-tests to examine EF and HRQOL scores by sex. Chi-square tests were used to compare the proportion of teens with executive dysfunction by adolescent vs. parent reports and by sex. Third, to address the main research question, we performed multivariable analyses adjusting for salient factors (i.e., age, sex, diabetes duration, and HbA1c) to assess the associations of executive dysfunction (independent variable) and HRQoL (dependent variable) by both adolescent self-report and parent proxy-report. To address our research question about sex dimorphism, we included an interaction term for sex by executive dysfunction in each model. Due to the multiple comparisons, p values <0.01 were considered statistically significant. We used SAS version 9.4 (SAS Institute, Cary, NC) to analyze the data.
Results
Participants’ characteristics
Adolescents (46% female) had a mean age of 15.9±1.3 years, diabetes duration of 8.4±3.7 years, and HbA1c of 8.5±1.2% (69±13 mmol/mol) (see Table 1). The majority of adolescents (67%) received insulin pump therapy. Of the parent participants, 83% were mothers and 64% had a college degree or higher (see Table 1). EF and HRQoL scores are presented in Table 2. Mean EF scores (based on the GEC scores) were 45.1±10.4 and 52.7±11.4 for adolescent self-reports and parent proxy-reports, respectively. Parent proxy-report of adolescent EF was significantly higher than adolescent self-report (t(167)=10.41, p<.0001). Mean EF scores by both adolescent self-reports and parent proxy-reports, respectively, did not differ significantly by sex. Mean HRQoL scores were 85.0±14.0 and 83.5±14.7 for adolescent self-reports and parent proxy-reports, respectively. There was not a significant difference between adolescent self-reported and parent proxy-reported adolescent HRQoL. Females had lower HRQoL than males by self-report (81.6±15.9 vs. 87.8±11.6, t(136.5)=−2.82, p=.006) and parent proxy-report (80.9±15.9 vs. 85.7±13.3, t(166)=−2.13, p=.03). Total scores on all variables, per adolescent self-report, and parent proxy-report, and scores for female versus male participants are presented in Table 2.
Table 1.
Adolescent Characteristics (N=169)
| Mean ± SD or % | |
|---|---|
| Age (years) | 15.9±1.3 |
| Sex (% female) | 46 |
| Race/ethnicity (% non-Hispanic white) | 88 |
| Family structure (% 2-parent family) | 87 |
| Parent respondent (% mother) | 83 |
| Parent college degree (%) | 64 |
| Type 1 diabetes duration (years) | 8.4±3.7 |
| Regimen (% pump treated) | 67 |
| BGM frequency (times/day) | 4.5±2.1 |
| HbA1c (mmol/mol; %) | 69±13; 8.5±1.2 |
| Parent proxy-report (%) | 32 |
Table 2.
Mean ± standard deviation scores for adolescent executive function, health-related quality of life
| All Participants (N=169) |
Female (n=77) |
Male (n=92) |
|||
|---|---|---|---|---|---|
| Executive Function | Parent proxy-report | Total | 52.7±11.4 | 51.0±11.5 | 54.0 ±11.1 |
| MI | 53.7±11.6 | 52.3±12.5 | 55.0±10.8 | ||
| BRI | 50.6±10.4 | 49.6±10.4 | 51.3±10.4 | ||
| Adolescent self-report | Total | 45.1±10.4 | 44.3±12.1 | 45.7±8.7 | |
| MI | 46.4±10.6 | 45.2±13.0 | 47.5±8.2 | ||
| BRI | 44.2±9.8 | 44.1±10.4 | 44.3±9.3 | ||
| HRQoL | Parent proxy-report | Total | 83.5±14.7 | 80.9±15.9 | 85.7±13.3 |
| Physical | 87.7±15.0 | 85.4±16.6 | 89.7±13.2 | ||
| Psychosocial | 81.2±16.2 | 78.4±17.1 | 83.6±15.1 | ||
| Adolescent self-report | Total | 85.0±14.0 | 81.6±15.9 | 87.8±11.6 | |
| Physical | 90.4±12.6 | 87.1±14.6 | 93.1±10.0 | ||
| Psychosocial | 82.1±16.1 | 78.7±17.7 | 85.0±14.3 | ||
MI=Metacognition Index; BRI=Behavioral Regulation Index
Associations of Adolescent and Parent Reports of Adolescent Executive Function and QoL
Adolescent self-reports and parent proxy-reports of EF were highly correlated (r = .62, p <.0001), as were adolescent self-reports and parent proxy-reports of adolescent HRQoL (r = .60, p <.0001) (see Table 3). Poorer adolescent EF as reported by both adolescent self-report and parent proxy-report was associated with poorer adolescent HRQoL by both adolescent self-report and parent proxy-report (r=−.62; r=−.64, respectively, both p <.0001). Similarly, poorer adolescent EF by self- and parent proxy-report was associated with poorer HRQoL on the Physical Functioning (adolescent self-report: r=−.51, parent proxy-report: r=−. 40, both p <.0001) and Psychosocial Functioning scales (adolescent self-report: r=−.62, parent proxy-report: r=−. 68, both p <.0001) (see Table 3).
Table 3.
Correlation Matrix for Adolescent Executive Function, Health-related Quality of Life, HbA1c, and Age
| Executive Function | Health-related Quality of Life | HbA1c | Age | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Parent proxy-report | Adolescent self-report | Parent proxy-report | Adolescent self-report | |||||||||||||
| Total | MI | BRI | Total | MI | BRI | Total | Physical | Psychosocial | Total | Physical | Psychosocial | |||||
| Executive Function | Parent proxy-report | Total | - | .97* | .88* | .62* | .60* | .55* | −.64* | −.40* | −.68* | −.42* | −.40* | −.41* | .28† | .02 |
| MI | - | .75* | .58* | .60* | .49* | −.60* | −.38* | −.65* | −.39* | −.38* | −.39* | .28† | −.01 | |||
| BRI | - | .60* | .51* | .61* | −.61* | −.40* | −.64* | −.44* | −.40* | −.42* | .24‡ | .10 | ||||
| Adolescent self-report | Total | - | .95* | .93* | −.50* | −.32* | −.54* | −.62* | −.50* | −.62* | .20§ | .15§ | ||||
| MI | - | .77* | −.46* | −.27† | −.50* | −.55* | −.45* | −.54* | .20‡ | .17§ | ||||||
| BRI | - | −.50* | −.34* | −.52* | −.63* | −.50* | −.63* | .17§ | .13 | |||||||
| Health-related Quality of Life | Parent proxy-report | Total | - | .85* | .97* | .61* | .54* | .58* | −.21‡ | −.03 | ||||||
| Physical | - | .69* | .47* | .47* | .43* | −.16§ | −.01 | |||||||||
| Psychosocial | - | .60* | .52* | .58* | −.22‡ | −.03 | ||||||||||
| Adolescent self-report | Total | - | .85* | .98* | −.15 | −.09 | ||||||||||
| Physical | - | .72* | −.15§ | −.15 | ||||||||||||
| Psychosocial | - | −.13 | −.05 | |||||||||||||
| HbA1c | - | .13 | ||||||||||||||
| Age | - | |||||||||||||||
Note.
p<.0001,
p<.001,
p<.01,
p<.05;
MI=Metacognition Index; BRI=Behavioral Regulation Index
The proportion of adolescents with executive dysfunction (defined as an elevated score on the GEC, BRI, or MI) differed significantly by adolescent and parent reports (13% vs. 32%; χ2 = 17.4, p<.0001). In adolescent self-reports, there was a similar proportion of males (12%) and females (14%) with executive dysfunction (χ2 = .20, p=.65). Parent proxy-reports of EF also identified similar rates of executive dysfunction in males (34%) and females (29%) (χ2 = .43, p=.51).
Associations of Adolescent and Parent Reports of Adolescent Executive Dysfunction and HRQoL: Impact of Sex
Next, we sought to understand the impact of adolescent executive dysfunction on adolescent self-reported and parent proxy-reported adolescent HRQoL. In multivariable analyses (see Table 4) adjusted for adolescent age, sex, duration of T1D, and HbA1c, 21% of the variance in adolescent self-reported HRQoL was explained by adolescent self-reported executive dysfunction (based on the cut-off score ≥60 on the Global Executive Composite, the Metacognition Index, and/or the Behavioral Regulation Index) (R2 =.21, p<.0001). Similarly, 24% of the variance in parent proxy-report of adolescent HRQoL was explained by parent proxy-report of adolescent executive dysfunction (R2 =.24, p<.0001) (See Table 4). As depicted in Figure 1, in adolescent self-report but not parent proxy-report, there was a significant interaction effect of executive dysfunction and sex on adolescent HRQoL (p=.009), with substantially lower HRQoL reported by females with executive dysfunction compared with males with executive dysfunction, but no difference by sex in those without executive dysfunction.
Table 4.
Multivariate Analyses
| Dependent variable: Adolescent self-report HRQoL |
Dependent variable: Parent proxy-report HRQoL |
|||
|---|---|---|---|---|
| Estimate | p | Estimate | p | |
| Intercept | 93.13 | <.0001 | 88.85 | <.0001 |
| Age | −0.53 | 0.48 | −0.54 | 0.48 |
| T1D duration | 0.37 | 0.16 | 0.19 | 0.48 |
| HbA1c | −1.09 | 0.19 | −0.70 | 0.42 |
| Sex (female vs. male) | −18.76 | 0.0004 | −7.77 | 0.03 |
| Executive dysfunction (no dysfunction vs. dysfunction) | 10.55 | 0.0096 | 14.77 | <.0001 |
| Sex × executive dysfunction | 14.83 | 0.0090 | 3.21 | 0.44 |
Note. The coefficients for adolescent self-reported HRQoL are for the analyses done with adolescent reported executive dysfunction; the coefficients for parent proxy-reported HRQoL are for the analyses done with parent proxy-reported executive dysfunction
Figure 1.
Interaction effect of executive dysfunction and sex on adolescent HRQoL (adjusted total PedsQL scores, controlling for age, T1D duration, and HbA1c). For self-report of EF and HRQoL, there was a significant interaction effect of executive dysfunction and sex on adolescent HRQoL (p=.009), with substantially lower HRQoL reported by females with executive dysfunction compared with males with executive dysfunction. White triangles with dashed line = females. Black squares with solid line = males. EF = executive function. HRQoL = health-related quality of life.
Discussion
Person-reported outcomes have emerged as important metrics to assess in the management of chronic diseases such as diabetes [20], and HRQoL has become one such important measure, especially given the unending and rigorous self-care demands of T1D [26]. The current research addresses the particular contribution that executive dysfunction may have on HRQoL in adolescents with T1D. Previous studies have mainly addressed the negative impact of EF problems in adolescents with T1D on self-care behaviors and subsequent glycemic control [12–14, 16], or have assessed the potential negative impact of the burdens of self-care on HRQoL [7, 8]. Moreover, most previous research on adolescent EF has included reports by a single respondent, either parents or youth [12–14, 16], with occasional exceptions [27, 28], leaving questions unanswered about potential differences across respondents. Previous studies have shown that parent proxy-report of their child’s HRQoL is often lower than self-report in youth with T1D [17, 18]. Although adolescents may be best able to report their internal states and feelings, parents’ perceptions often influence healthcare utilization. Therefore, comprehensive assessment of adolescent HRQoL should include the perspectives of both adolescents and parents [19, 29].
In our sample of 169 adolescents with T1D and their parents, parents reported significantly more EF problems than their adolescents, while there were no differences between parent proxy-reports and adolescent self-reports of HRQoL. In total, about 1 out of 8 (13%) adolescents had executive dysfunction by self-report compared with nearly 1 out of 3 (32%) adolescents meeting criteria for executive dysfunction by parent proxy-reports. The observation that parents reported significantly greater adolescent EF problems than adolescents did is in line with previous literature reporting on adolescent self- and parent proxy-reports of adolescent EF [28], and with the tendency of parents to over-report child problems and adolescents potentially to underreport problems. A notable exception includes a study that included similar EF scores by both adolescent self-report and parent proxy-report [27]. However, this study by Suchy et al. focused on older adolescents (mean age 17.7) who may have a more accurate estimation of their own competencies than younger adolescents [27].
The percentage of adolescents with executive dysfunction is notably higher in our study than in previous research. For example, in the study of Perez and colleagues [16], the prevalence of elevated scores on EF skills ranged from 11 to 18.6%. In that study, the clinical cut-off score of ≥65 was used to indicate clinically impaired EF. Others have used the cut-point of 60, such as the report by Roth and colleagues [23]; a threshold that we chose as indicative for executive dysfunction as we wanted to include adolescents with any degree of executive dysfunction, including those in the mildly elevated range (60 to <65) and clinically elevated range (≥65), in order to include a broader spectrum of adolescents who may be at risk.
Further, from both adolescents’ and parents’ perspectives, there was a significant relationship between executive dysfunction and poorer adolescent HRQoL. This finding is in line with a previous study that examined the link between EF and HRQoL in adolescents with T1D; Perez and colleagues reported that parent-reported executive dysfunction was related to lower adolescent HRQoL [16]. Our study confirms this association in a cohort of adolescents with mildly, as well as clinically elevated executive dysfunction, and by including adolescent as well as parent reports of both EF and HRQoL.
Interestingly, with regard to sex differences, while the proportion of males and females with executive dysfunction was similar in adolescent and parent reports, by both adolescent self-reports and parent proxy-reports, female adolescents scored significantly lower on HRQoL than male adolescents. Further, we identified a significant interaction effect of executive dysfunction on HRQoL only in females, suggesting that females may be more vulnerable to a negative impact of executive dysfunction on HRQoL than males. This sexual dimorphism deserves attention, as it is in line with previous findings of females being more likely to internalize problems [30] and, in turn, seek more mental health support than males.
Strengths and Limitations
Our study investigates the relatively unique impact of executive dysfunction on HRQoL in adolescents with T1D from a multi-informant perspective in a fairly large sample of adolescents and their parents. Despite the novelty of this study, some limitations need to be acknowledged, providing potential avenues for future research. First, the cross-sectional design prevents interpretation of causality. For example, although the current findings suggest a negative impact of executive dysfunction on HRQoL in adolescents with T1D, future longitudinal research could confirm the directionality of this association. Second, given the relatively higher functioning of our study sample (e.g., higher frequency of pump use), it is unclear whether these findings would be generalizable to adolescents with a broader range of glycemic control and with less access to technology. Hence, replication of results in a longitudinal study design, and across clinical centers, may be more representative in future research of adolescents with T1D. Third, for the current study we used a generic measure of HRQoL. To better understand the extent to which executive dysfunction impacts diabetes-specific HRQoL in adolescents with T1D, future research should also use validated diabetes-specific measures of HRQoL [31].
Clinical Implications
Our findings provide interesting opportunities for clinical practice with potential to assess EF as well as HRQoL in adolescents with T1D. EF can be assessed by questionnaires [23, 32] or performance-based measures [27, 33]; HRQoL can similarly be assessed by easy-to-administer validated surveys [22, 34]. Given identification of executive dysfunction in adolescents with T1D, one can then provide resources to address potential deficiencies in self-care behaviors for the management of T1D as well as mental health support to address the potential negative impact on HRQoL, especially in females. Indeed, clinicians can assist those with executive dysfunction by focusing on competencies related to planning and organization around self-care behaviors and include conversations with adolescents addressing their HRQoL. As noted previously, discrepancies between adolescent and parent perspectives of adolescents’ HRQoL, and EF are not uncommon [19]. Therefore, clinicians should assess both perspectives when addressing these two important clinical outcomes [19]. When clinically significant discrepancies occur, clinicians should consider potential causes [35], such as different response styles (e.g., adolescents may be more likely to choose extreme response options) [36].
Acknowledgements
The authors would like to thank the participating adolescents and their families.
Funding
This research was supported by NIH grants R01DK095273, K12DK094721, and P30DK036836; JDRF grant 2-SRA-2014-253-M-B; the Katherine Adler Astrove Youth Education Fund; the Maria Griffin Drury Pediatric Fund; and the Eleanor Chesterman Beatson Fund. Dr. Goethals’ work on this project was supported by the Belgian American Educational Foundation (BAEF) and a Mary K. Iacocca Research Fellowship provided by the Iacocca Family Foundation.
Footnotes
Conflicts of interest
We have no relevant conflict of interest to disclose related to the current study. L.M.L. reports consultative work with Boehringer Ingelheim, Convatec, Dexcom, Insulet, Insulogic, Janssen Pharmaceuticals, Laxmi, LifeScan, Medtronic, Novo Nordisk, Roche Diagnostics, Sanofi.
Ethics Approval
All procedures performed in this study were in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
Consent to participate
Informed consent/assent was obtained from all participants in the study prior to implementation to any study procedures. Research procedures were approved by the Institutional Review Board of the Joslin Diabetes Center (Boston, MA, USA).
References
- 1.Patterson CC, et al. , Worldwide estimates of incidence, prevalence and mortality of type 1 diabetes in children and adolescents: Results from the International Diabetes Federation Diabetes Atlas, 9th edition. Diabetes Res Clin Pract, 2019. 157: p. 107842. [DOI] [PubMed] [Google Scholar]
- 2.Atkinson MA, Eisenbarth GS, and Michels AW, Type 1 diabetes. Lancet, 2014. 383(9911): p. 69–82. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Wiebe DJ, et al. , Developmental processes associated with longitudinal declines in parental responsibility and adherence to type 1 diabetes management across adolescence. J Pediatr. Psychol, 2014. 39(5): p. 532–541. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Hermann JM, et al. , The Transatlantic HbA1c gap: differences in glycaemic control across the lifespan between people included in the US T1D Exchange Registry and those included in the German/Austrian DPV registry. Diabet Med, 2019. [DOI] [PubMed] [Google Scholar]
- 5.Oris L, et al. , Illness Identity in Adolescents and Emerging Adults With Type 1 Diabetes: Introducing the Illness Identity Questionnaire. Diabetes Care, 2016. 39(5): p. 757–63. [DOI] [PubMed] [Google Scholar]
- 6.Raymaekers K, et al. , The Role of Peers for Diabetes Management in Adolescents and Emerging Adults With Type 1 Diabetes: A Longitudinal Study. Diabetes Care, 2017. 40(12): p. 1678–1684. [DOI] [PubMed] [Google Scholar]
- 7.Hilliard ME, et al. , How poorer quality of life in adolescence predicts subsequent type 1 diabetes management and control. Patient Educ Couns, 2013. 91(1): p. 120–5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Anderson BJ, et al. , Factors Associated With Diabetes-Specific Health-Related Quality of Life in Youth With Type 1 Diabetes: The Global TEENs Study. Diabetes Care, 2017. 40(8): p. 1002–1009. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Group W, The World Health Organization quality of life assessment (WHOQOL): position paper from the World Health Organization. Social science & medicine, 1995. 41(10): p. 1403–1409. [DOI] [PubMed] [Google Scholar]
- 10.Dictionary of Quality of Life and Health Outcomes Measurement. 1st Ed. ed, ed. Mayo N. 2015, Milwaukee, WI. 180. [Google Scholar]
- 11.Goethals ER, et al. , Assessing Readiness for Independent Self-Care in Adolescents with Type 1 Diabetes: Introducing the RISQ. Diabetes Res Clin Pract, 2020: p. 108110. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Goethals ER, et al. , Child and parental executive functioning in type 1 diabetes: Their unique and interactive role toward treatment adherence and glycemic control. Pediatr Diabetes, 2018. 19(3): p. 520–526. [DOI] [PubMed] [Google Scholar]
- 13.Vloemans AF, et al. , Youth with type 1 diabetes taking responsibility for self-management: the importance of executive functioning in achieving glycemic control: results from the longitudinal DINO study. Diabetes Care, 2019. 42(2): p. 225–231. [DOI] [PubMed] [Google Scholar]
- 14.Berg CA, et al. , Executive Function Predicting Longitudinal Change in Type 1 Diabetes Management During the Transition to Emerging Adulthood. Diabetes Care, 2018. 41(11): p. 2281–2288. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Ratiu I, et al. , Executive function and quality of life in individuals with Marfan syndrome. Qual Life Res, 2018. 27(8): p. 2057–2065. [DOI] [PubMed] [Google Scholar]
- 16.Perez KM, et al. , Executive function in adolescents with type 1 diabetes: Relationship to adherence, glycemic control, and psychosocial outcomes. J Pediatr Psychol, 2017. 42(6): p. 636–646. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Yi-Frazier JP, et al. , Whose quality of life is it anyway? Discrepancies between youth and parent health-related quality of life ratings in type 1 and type 2 diabetes. Qual Life Res, 2016. 25(5): p. 1113–21. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Kalyva E, et al. , Health-related quality of life (HRQoL) of children with type 1 diabetes mellitus (T1DM): self and parental perceptions. Pediatr Diabetes, 2011. 12(1): p. 34–40. [DOI] [PubMed] [Google Scholar]
- 19.Eiser C and Varni JW, Health-related quality of life and symptom reporting: similarities and differences between children and their parents. Eur J Pediatr, 2013. 172(10): p. 1299–304. [DOI] [PubMed] [Google Scholar]
- 20.de Wit M, et al. , Using Person-Reported Outcomes (PROs) to Motivate Young People with Diabetes. Curr Diab Rep, 2020. 20(7): p. 23. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.de Wit M, et al. , Assessing diabetes-related quality of life of youth with type 1 diabetes in routine clinical care: the MIND Youth Questionnaire (MY-Q). Pediatr Diabetes, 2012. 13(8): p. 638–46. [DOI] [PubMed] [Google Scholar]
- 22.Hilliard ME, et al. , Assessing Health-Related Quality of Life in Children and Adolescents with Diabetes: Development and Psychometrics of the Type 1 Diabetes and Life (T1DAL) Measures. J Pediatr Psychol, 2020. 45(3): p. 328–339. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Roth RM, Isquith PK, and Gioia GA, Assessment of Executive Functioning Using the Behavior Rating Inventory of Executive Function (BRIEF), in Handbook of Executive Functioning, Goldstein S and Naglieri JA, Editors. 2014, Springer Science+Business Media: New York. p. 301–331. [Google Scholar]
- 24.Varni JW, Seid M, and Kurtin PS, PedsQL 4.0: reliability and validity of the Pediatric Quality of Life Inventory version 4.0 generic core scales in healthy and patient populations. Med Care, 2001. 39(8): p. 800–812. [DOI] [PubMed] [Google Scholar]
- 25.Harris PA, et al. , Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support. J. Biomed. Inform, 2009. 42(2): p. 377–381. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Anderson LM, et al. , Patient-Reported and Parent Proxy-Reported Outcomes in Pediatric Medical Specialty Clinical Settings: A Systematic Review of Implementation. J Pediatr Psychol, 2019. [DOI] [PubMed] [Google Scholar]
- 27.Suchy Y, et al. , The relation of questionnaire and performance-based measures of executive functioning with type 1 diabetes outcomes among late adolescents. Health Psychol, 2016. 35(7): p. 661–669. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Duke DC, Raymond JK, and Harris MA, The Diabetes Related Executive Functioning Scale (DREFS): pilot results. Children’s Health Care, 2014. 43(4): p. 327–344. [Google Scholar]
- 29.Cheng C-P, et al. , Agreement of children and parents scores on Chinese version of Pediatric Quality of Life Inventory Version 4.0: Further psychometric development. Applied Research in Quality of Life, 2016. 11(3): p. 891–906. [Google Scholar]
- 30.Luyckx K, Seiffge-Krenke I, and Hampson SE, Glycemic control, coping, and internalizing and externalizing symptoms in adolescents with type 1 diabetes: a cross-lagged longitudinal approach. Diabetes Care, 2010. 33(7): p. 1424–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Varni JW, et al. , PedsQL 3.2 Diabetes Module for Children, Adolescents, and Young Adults: Reliability and Validity in Type 1 Diabetes. Diabetes Care, 2018. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Lewin AB, et al. , Validity and reliability of an adolescent and parent rating scale of type 1 diabetes adherence behaviors: the Self-Care Inventory (SCI). J Pediatr Psychol, 2009. 34(9): p. 999–1007. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Hamburger ER, et al. , Performance-based and questionnaire measures of executive function in adolescents with type 1 diabetes. J Behav Med, 2019. 42(6): p. 1041–1049. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.de Wit M, et al. , Monitoring and discussing health-related quality of life in adolescents with type 1 diabetes improve psychosocial well-being: a randomized controlled trial. Diabetes Care, 2008. 31(8): p. 1521–1526. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Van Roy B, et al. , Understanding discrepancies in parent-child reporting of emotional and behavioural problems: Effects of relational and socio-demographic factors. BMC Psychiatry, 2010. 10: p. 56. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Davis E, et al. , Parent-proxy and child self-reported health-related quality of life: using qualitative methods to explain the discordance. Qual Life Res, 2007. 16(5): p. 863–71. [DOI] [PubMed] [Google Scholar]