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Impact of family‐centered tailoring of pediatric diabetes self‐management resources

Abstract Background The American Diabetes Association recommends a family‐centered approach that addresses each family's specific type 1 diabetes self‐management barriers. Objective To assess an intervention that tailored delivery of self‐management resources to families' specific self‐management barriers. Subjects At two sites, 214 children 8‐16 years old with type 1 diabetes and their parent(s) were randomized to receive tailored self‐management resources (intervention, n = 106) or usual care (n = 108). Methods Our intervention (1) identified families' self‐management barriers with a validated survey, (2) tailored self‐management resources to identified barriers, and (3) delivered the resources as four group sessions coordinated with diabetes visits. Mixed effects models with repeated measures were fit to A1c as well as parent and child QOL during the intervention and 1 year thereafter. Results Participants were 44% youth (8‐12 years) and 56% teens (13‐16 years). No intervention effect on A1c or QOL was shown, combining data from sites and age groups. Analyzing results by site and age group, post‐intervention A1c for teens at one site declined by 0.06 more per month for intervention teens compared to usual care (P < 0.05). In this group, post‐intervention A1c declined significantly when baseline A1c was >8.5 (−0.08, P < 0.05), with an even larger decline when baseline A1c was >10 (−0.19, P < 0.05). In addition, for these teens, the significant improvements in A1c resulted from addressing barriers related to motivation to self‐manage. Also at this site, mean QOL increased by 0.61 points per month more during the intervention for parents of intervention youth than for usual care youth (P < 0.05). Conclusions Tailored self‐management resources may improve outcomes among specific populations, suggesting the need to consider families' self‐management barriers and patient characteristics before implementing self‐management resources.


| INTRODUCTION
Behavioral interventions to improve self-management, glycemic control, or quality of life (QOL) for children with type 1 diabetes have small to moderate positive effects, 1-5 but a host of challenges diminishes their effectiveness in practice. 6 As a result, the impact of these interventions on the health and well-being of children with type 1 diabetes does not reach its potential. In fact, less than 20% of the US children with type 1 diabetes between the ages of 6-17 years meet recommended hemoglobin A1c (A1c) goals. 7 Most behavioral interventions in type 1 diabetes were developed and evaluated within the ideal conditions of clinical trials, often resulting in interventions that are difficult to implement and sustain in real-world clinical settings. Lack of healthcare system infrastructure and resources (eg, payment systems, trained personnel, and time constraints), as well as inadequate buy-in coupled with increased burden for families (eg, extra visits and missed school or work) have limited the feasibility, effectiveness, and sustainability of the interventions. 1,2,5,6 Among families for whom self-management is already problematic, completing lengthy, intensive interventions may not be feasible or acceptable. 8,9 Most approaches to improving self-management behaviors rely on either lifestyle interventions or education, which may be combined with support. Meta-analyses of behavioral interventions to improve self-management, glycemic control, or quality of life have shown small to moderate effects. [1][2][3][4][5] For example, motivational interviewing can significantly reduce A1c (by 0.5-1.5%) and improve QOL. 10,11 The largest effects are often achieved by multi-modal interventions that may incorporate behavioral and educational strategies as well as family therapy or social support. 1,2 However, these approaches are often resource intensive. 1 For example, in one trial, Behavioral Family Systems Therapy for Diabetes was delivered as 12 sessions over a 6-month period. 12 Among families for whom adherence is already challenging, it may not be feasible to complete lengthy, intensive interventions, especially when not coordinated with routine diabetes visits. 8,9 To be most effective and sustainable, diabetes care should be "patient-centered." 13 Patient-centered care addresses and is delivered in a manner that meets patients' and families' needs and preferences. 14 For children with type 1 diabetes and their parents, these needs revolve around barriers to self-management that are associated with worse glycemic control and quality of life. 15,16 Common selfmanagement challenges faced by individual families such as difficult family interactions, lack of motivation, and limited ability to understand or organize diabetes care can be efficiently identified with the Problem Recognition in Illness Self-Management (PRISM) survey tool.
The challenges identified by PRISM can then guide the selection among efficacious self-management resources that are routinely available within multidisciplinary pediatric diabetes clinics, such as diabetes education or psychological support. [1][2][3] This study evaluates the hypothesis that delivering self-management resources in a familycentered manner, using PRISM to guide resource selection, will result in better glycemic control and quality of life for children and their parents.

| Study overview
The design and methods of this trial have been published in detail elsewhere. 17 In brief, a randomized parallel arm clinical trial was conducted at two clinical sites, with a 12-month intervention period and 1 year of follow-up in the post-intervention period. The trial compared the effectiveness of family-centered tailoring of diabetes selfmanagement resources (intervention) to that of usual care. Primary outcomes were glycemic control and QOL for children and their parents. The family-centered intervention included: (1) identifying the family's self-management barriers using a validated survey tool, Problem Recognition in Illness Self-Management (PRISM); 15,18 (2) use of PRISM results to tailor self-management resources to the family's self-identified needs; and (3) delivery of the tailored resources in group sessions coordinated with routine diabetes clinic visits.

| Participants
Children aged 8-16 years were recruited from two large multidisciplinary diabetes clinics serving diverse urban and rural populations. Other eligibility criteria were (1) type 1 diabetes for at least 1 year, (2) English fluency, and (3) ability to provide consent/assent. Exclusion criteria were (1) parent survey response indicating that the child had a cognitive or mental health issue that might limit potential to benefit from group-based activities, (2) known hemoglobinopathy or medical condition that might affect A1c accuracy, (3) current participation in a diabetes intervention study or the pilot for this trial, (4) inability to continue care with the same clinician during the study, (5) inability of the consenting parent/guardian to accompany their child to all visits during the study, (6) study appointments unavailable within 4 months of enrollment date, and (7) no selfmanagement barrier identified with PRISM. To improve generalizability, baseline A1c values were not used to determine eligibility.
Teens ages 13-16 years and their parents completed PRISM to identify their self-management barriers. For youth (ages 8-12 years), only the parent completed PRISM. PRISM assessed the three self-management barriers addressed in the study: (1) understanding and organizing care, (2) motivation, and (3) family interactions. 15,18 Families for whom PRISM identified at least one self-management barrier were block randomized (by site and age group) to intervention or usual care on a 1:1 ratio using a computer program. Diabetes providers were not notified of the family's enrollment and had no access to the PRISM scores.

| Intervention and usual care (control)
The study intervention consisted of using results from the PRISM surveys to determine the type of self-management resource assigned to address one of three specific barriers (Motivation, Understanding and Organizing Care, and Family Interactions) identified by each family. Families who identified only one self-management barrier were scheduled for the next available group session that addressed that specific barrier. Families who identified multiple barriers were scheduled for the next available group session that addressed one of their barriers. If the teen and parent indicated different barriers, the teen's responses received priority. Trained professional facilitators delivered the resource content on the day of the child's routine diabetes clinic visit, either immediately before or after the clinic visit. Over a 9-month period, each intervention arm family received usual care and was offered four 75-minute self-management resource sessions. Sessions were delivered in an interactive, small-group format (two to six parent-child dyads). Participants stayed within the same small group throughout the intervention. Families received reminder calls 3 weeks and 1 week before each clinic appointment/coordinated group session.
All facilitators received standard materials and participated in initial training as well as facilitator meetings to share ideas, address concerns, and review fidelity data, either in person or via videoconferencing. The full group session content and all materials have been published in a free toolkit (available at https://www. hipxchange.org/T1DSMART). 19 In brief, barriers related to understanding and organizing care included challenges with the family's understanding of diabetes or diabetes self-management and the ability to incorporate self-management skills into daily living. Registered nurses and certified diabetes educators facilitated the "Tips and Tools" groups that addressed this barrier, using content derived from the American Diabetes Association's education curriculum. 20 Motivation barriers encompassed challenges with readiness to self-manage, including beliefs about the costs and benefits of self-management, the importance of self-management, as well as positive and negative aspects of the self-management regimen. 21 Certified members of the Motivational Interviewing Network of Trainees facilitated the "Your Diabetes, Your Choices" groups that addressed this barrier, using content based on the tenets of motivational interviewing. 10,11,22 Barriers related to family interactions reflected the challenges of balancing child autonomy with the role of family support and supervision. Pediatric health psychologists facilitated the "Family Teamwork" groups that addressed this barrier using content that followed a Behavioral Family Systems Therapy approach. 12,23,24  Families randomized to usual care continued to attend clinic visits with their multi-disciplinary diabetes care teams every 3 months.
Referral to certified diabetes educators, nutritionists, and mental health professionals continued per usual for both usual care and intervention participants, with referrals typically initiated based on individual provider judgment. Clinic appointments reminders were the same as for intervention participants in number and timing.

| Study outcomes and data collection
Point of care A1c data were obtained from medical records at nine time points (at baseline and then quarterly during the intervention (3, 6, 9, and 12 months) and during the post-intervention period (15,18,21, and 24 months) and standardized to DCCT-equivalent values. 17 QOL data were collected at five time points: at baseline, at 3 months after the first intervention session, and at 3, 6, and 9 months post-intervention. Child QOL was measured by the total scale score for the diabetes-specific PedsQL. 26  In addition, enrollment surveys assessed participant characteristics and disease or regimen factors that have known or hypothesized relationships to glycemic control, self-management barriers, or QOL. [30][31][32][33][34][35][36][37][38][39] Participant characteristics included parent/child age (continuous) and gender, race/ethnicity (standard categories, aggregated as white, non-Hispanic vs all other), the child's health status, child's comorbid conditions, and parent's education (12th grade or less, high school graduate, some college, bachelor's degree, graduate, or professional degree).
Disease and regimen factors included years since diagnosis and device use (eg, insulin pumps, continuous glucose monitors, or combinations of these functions).

| Analyses
Primary analyses compared outcomes between intervention and usual care arms as defined by the intention-to-treat principle. Trajectories were fit for A1c, and for QOL for children and parents, using mixed effects models with within-person correlation from repeated measures captured by individual random intercepts and slopes. The effect of intervention in these models is captured by the coefficients of intervention by time trend interaction, which represents the difference in the trajectory of the outcome per month between the two study arms. In overall analyses, fixed effects were included in the models to represent randomization strata (eg, clinic site and age group), and their interactions with time trend were tested. A limited set of additional variables were included in the model to correct for imbalance between usual care and intervention arms. Addition of covariates (eg, baseline demographics, healthcare utilization, and comorbidities) to improve precision did not substantively change study findings; therefore, we present results without additional covariate adjustment. This study was powered to detect minimally clinically significant intervention effects on glycemic control and QOL, with a two-tailed alpha = 0.05.
Detailed power calculations are published elsewhere. 17 The same models were fit within each of the four randomization strata as planned a priori and within baseline A1c (≤8.5%, >8.5-10% or <10%). Significant results in the A1c trend analyses were confirmed with cross-sectional analyses, for which we report mean A1c values. Furthermore, pattern mixture models, used to evaluate any influence of missing data, did neither result in any substantive changes in the effects seen nor alter the statistical significance of any model parameters. Lastly, to further explain any significant site and age group differences in treatment effect, a post hoc analysis of the impact of each of the three types of tailored intervention content (eg, Tips and Tools; Your Diabetes, Your Choices; or Family Teamwork) was undertaken when intervention and usual care arms had at least 10 participants.

| Recruitment and retention
Our published protocol provides details of recruitment and participant flow, including a CONSORT diagram. 17 A total of 214 families were recruited and randomized, 108 to usual care, and 106 to the intervention. More youth were enrolled at Site 1 than at Site 2 (52 vs 42, P < 0.05); and significantly fewer teens enrolled at Site 1 than at Site 2 (48 vs 71, P < 0.05). Significantly more families at Site 2 were excluded based on study criteria than at Site 1 (84% vs 65%, P < 0.01), but reasons for exclusion were similar at the two sites. Three families withdrew during the study, two in the intervention arm and one in usual care. No families were lost to follow-up, defined as not contributing study data after the baseline enrollment.
As reported in detail elsewhere, the clinical sites and participant genders and age groups were equally represented in the study. 17 The mean A1c at recruitment was 9.1%, and less than 15% of A1c values met the American Diabetes Association's target of <7.5%.
Parents who enrolled tended to be white, non-Hispanic mothers with varying levels of education. The most commonly identified selfmanagement barrier was motivation. Randomization achieved balance on all but two characteristics. Compared to the usual care arm, the intervention arm had significantly fewer mothers enrolled and more participants using insulin pumps. Of note, the study arms were balanced at baseline with regard to each of the three study outcome measures.
Between the study sites, participant demographics and diabetesrelated characteristics did not differ significantly, with three exceptions. Site 2 had significantly fewer non-Hispanic, white participants (76% vs 93%, P < 0.01) and fewer participants using pumps (44% vs 59%, P < 0.05), than Site 1. Although the mean baseline A1c was not significantly different between the sites, Site 2 had significantly more participants with A1c <7.5% than Site 1 (20% vs 7%, P < 0.01). This site-based difference was driven largely by teens, as Site 2 had significantly more teens (ages 13-16) with A1c <7.5%, than Site 1 (23% vs 4%, P < 0.01). Examining A1c by site, within age groups, and by study arm, we find that among teens receiving usual care, Site 2 had significantly more participants with A1c <7.5% than Site 1 (29% vs 4%, P < 0.05), whereas intervention arms from the two sites were similar (Table 1).

| Group session fidelity and attendance
Overall, the group sessions had high fidelity (mean 93%), with nearly half of sessions examined having a fidelity score of 100%. Fidelity did not differ significantly by site (92% vs 94%, P = 0.28), but differed by age group, with higher fidelity in the teen groups than in youth (95% vs 89%, P < 0.01).
The vast majority of families (82%) attended at least half of the group sessions (69% attended three of the four and 47% attended all four sessions). Attendance did not differ significantly by site.
Among youth participants, only four families (two at each site) did not attend even a single intervention session. Among teen participants, eight families did not attend any intervention session, and all eight of these were at Site 2. These eight teens were similar to other intervention teens at this site (n = 28) in their baseline A1c, use of insulin pumps, time needed to travel to the diabetes clinic, and gender of parent who consented and agreed to attend the intervention group session, as well as the barriers they experienced. However, parental educational attainment was significantly lower among these eight non-attenders than for other intervention teens at this site.

| Impact of the intervention on A1c
Combining data from sites and age groups, we found no significant overall intervention effect on A1c trajectory, neither during the intervention (0.01, 95% CI −0.02 to 0.03) nor postintervention (−0.01, 95% CI −0.03 to 0.01) periods. However, intervention arm teens (ages 13-16 years) at Site 1 had a significantly better A1c trajectory post-intervention (mean A1c declined by 0.02 per month) than teens receiving usual care (mean A1c rose by 0.04 per month; Figure 1). Taken together, for intervention arm teens at Site 1, mean Alc trajectory was 0.06 lower per month than teens receiving usual care at that site (−0.06, 95% CI −0.11 to −0.01). As a consequence of the difference in trajectories, we found in cross-sectional analyses that A1c for teens at Site 1 at the conclusion of the post-intervention period was significantly lower for intervention participants than for those receiving usual care (9.68% vs 10.76%, P < 0.05). In cross-sectional analyses, A1c for usual care teens at this site rose by 0.5 (from 10.26 to 10.76) during this period, while intervention teens' A1c declined by 0.13 (from 9.81 to 9.68).
To further understand why results differed by intervention site, we examined whether the intervention's impact differed by glycemic control at baseline, which was known to differ at the two sites. Lastly, we found evidence that specific session types were responsible for the age and site-specific intervention impacts on A1c. Among

| Impact of the intervention on QOL for the child with diabetes
Our analyses demonstrated no significant overall intervention effect on QOL trajectories for the child, during the intervention (0.02, 95% CI −0.25 to 0.30) or post-intervention (−0.07, 95% CI −0.52 to 0.37) periods, when combining data from sites and age groups. There was also no significant intervention effect within sites by age groups.  F I G U R E 2 QOL trajectory by study arm and site for parents of teens (13-16 years) and youth (8-12 years) intervention period were greatest for children with higher baseline A1c values. In addition, improvements in A1c were largely due to the resources that addressed barriers related to motivation and to understanding and organizing diabetes care.

| Impact of the intervention on QOL for the parent of a child with diabetes
Our results suggest that healthcare systems or clinics may want to consider their capacity to deliver resources and also the characteristics of their patient population prior to implementing selfmanagement resources. For organizations with capacity to offer only one resource, the Your Diabetes, Your Choices content that addresses motivation to self-manage is promising because it addresses the most common self-management barrier among children 8 Thus, site-based differences in A1c at baseline may explain why similar A1c trajectories for teens at Site 2 did not reach significance, as they did for Site 1.
Although this trial has many strengths, there are some notable limitations. Our trial was conducted at two clinical sites, which could limit the generalizability of findings. In addition, we excluded families who did not speak or read English, and we did not recruit families when parents believed their child might be uncomfortable participating in the trial's group-based activities. Despite these factors, participating families' characteristics were similar to those of the US population of children with type 1 diabetes. In addition, although we did not inform providers of their patients' participation, it is possible that families discussed it with them, which could result in intensification of the usual care provided, limiting our ability to detect impact for our intervention. Our results with regard to A1c trajectories suggest that additional work could be done to potentially improve the impact of the approach for youth. For example, developing a survey to identify selfmanagement barriers from the perspective of the youth, rather than the parent may improve impact. Alternatively, the intervention content and materials may need adaptation to better meet the needs of youth, as intervention fidelity was lower for youth. Future work could also investigate whether any intervention effect is due solely to the increased contact time for intervention group families. Lastly, increasing the intensity of the intervention could improve its impact, especially if this can be achieved through telehealth platforms that limit burden from travel to the clinic.
In conclusion, our findings suggest that family-centered tailoring of type 1 diabetes self-management resources may benefit specific youth and teens, especially those with A1c values >8.5% and those with barriers related to either understanding and organizing their selfmanagement or their motivation to self-manage. In addition, our process for creating buy-in and coordinating self-management help with routine clinic visits resulted in high rates of uptake for the intervention. Disseminating the information gained from this study could support healthcare organizations in effectively and efficiently make use of existing but limited healthcare system resources to support self-management. For example, access to this family-centered approach could be broadened by adapting the intervention for use via videoconferencing or other similar technologies.