A mobile app identifies momentary psychosocial and contextual factors related to mealtime self-management in adolescents with type 1 diabetes.

Effective diabetes problem solving requires identification of risk factors for inadequate mealtime self-management. Ecological momentary assessment was used to enhance identification of factors hypothesized to impact self-management. Adolescents with type 1 diabetes participated in a feasibility trial for a mobile app called MyDay. Meals, mealtime insulin, self-monitored blood glucose, and psychosocial and contextual data were obtained for 30 days. Using 1472 assessments, mixed-effects between-subjects analyses showed that social context, location, and mealtime were associated with missed self-monitored blood glucose. Stress, energy, mood, and fatigue were associated with missed insulin. Within-subjects analyses indicated that all factors were associated with both self-management tasks. Intraclass correlations showed within-subjects accounted for the majority of variance. The ecological momentary assessment method provided specific targets for improving self-management problem solving, phenotyping, or integration within just-in-time adaptive interventions.

Sleep in Teens With Type 1 Diabetes: Perspectives From Adolescents and Their Caregivers.

PURPOSE: The purpose of this study is to identify barriers, facilitators, and consequences of obtaining sufficient sleep in adolescents with type 1 diabetes. METHODS: Semistructured interviews were conducted with 25 adolescents (52% female, mean age = 15.6 years) and 25 caregivers. Interviews were transcribed and coded using Atlas.ti. A thematic analytic approach was used to identify and organize significant patterns of meaning (themes) and interpret themes across the data. RESULTS: Several barriers were identified, with the most common being the use of electronics before bed and sleep disturbances related to diabetes management. Caregivers described strategies for helping adolescents achieve sufficient sleep, such as enforcing bedtimes and limiting distractions, but many adolescents could not identify facilitators of sleep. Weekday/weekend discrepancies in sleep timing were commonly disclosed. CONCLUSIONS: This study is the first to examine the perceptions of barriers and facilitators to obtaining sufficient sleep in adolescents with T1D and their caregivers. Results have the potential to inform providers' recommendations regarding sleep, including possible interventions to promote sleep in this high-risk population.

Mobile Momentary Assessment and Biobehavioral Feedback for Adolescents with Type 1 Diabetes: Feasibility and Engagement Patterns.

BACKGROUND: Integration of momentary contextual and psychosocial factors within self-management feedback may provide more specific, engaging, and personalized targets for problem solving. METHODS: Forty-four youth ages 13-19 with type 1 diabetes (T1D) were provided a Bluetooth meter and completed the 30-day protocol. Participants were randomized to "app + meter" or "meter-only" groups. App + meter participants completed mealtime and bedtime assessment each day. Assessments focused on psychosocial and contextual information relevant for self-management. Graphical feedback integrated self-monitored blood glucose (SMBG), insulin, and Bluetooth-transmitted blood glucose data with the psychosocial and contextual data. App + meter participants completed an interview to identify data patterns. RESULTS: The median number of momentary assessments per participant was 80.0 (range 32-120) with 2.60 per day. By 2 weeks participants had an average of 40.77 (SD 12.23) assessments. Dose-response analyses indicated that the number of app assessments submitted were significantly related to higher mean daily SMBG (r = -0.44, P < 0.05) and to lower% missed mealtime SMBG (r = -0.47, P < 0.01). Number of feedback viewing sessions was also significantly related to a lower% missed mealtime SMBG (r = -0.44, P < 0.05). Controlling for baseline variables, mixed-effects analyses did not indicate group × time differences in mean daily SMBG. Engagement analyses resulted in three trajectory groups distinguished by assessment frequencies and rates of decline. Engagement group membership was significantly related to gender, mean daily SMBG, and HbA1c values. CONCLUSIONS: Momentary assessment combined with device data provided a feasible means to provide novel personalized biobehavioral feedback for adolescents with T1D. A 2-week protocol provided sufficient data for self-management problem identification. In addition to feedback, more intensive intervention may need to be integrated for those patients with the lowest self-management at baseline.

Use of Commonly Available Technologies for Diabetes Information and Self-Management Among Adolescents With Type 1 Diabetes and Their Parents: A Web-Based Survey Study.

BACKGROUND: For individuals with Type 1 diabetes (T1D), following a complicated daily medical regimen is critical to maintaining optimal health. Adolescents in particular struggle with regimen adherence. Commonly available technologies (eg, diabetes websites, apps) can provide diabetes-related support, yet little is known about how many adolescents with T1D use them, why they are used, or relationships between use and self-management. OBJECTIVE: This study examined adolescent and parent use of 5 commonly available technologies for diabetes, including proportions who use each technology, frequency of use, and number of different technologies used for diabetes. Analyses also investigated the reasons adolescents reported for using or not using technologies for diabetes, and factors correlated with adolescents' technology use. Finally, this study examined relationships between the type and number of technologies adolescents use for diabetes and their self-management and glycemic control. METHODS: Adolescents (12-17 years) and their parents (N=174 pairs), recruited from a pediatric diabetes clinic (n=134) and the Children with Diabetes community website (n=40), participated in this Web-based survey study. Glycosylated hemoglobin (A1C) values were obtained from medical records for pediatric clinic patients. Adolescents reported their use of 5 commonly available technologies for diabetes (ie, social networking, diabetes websites, mobile diabetes apps, text messaging, and glucometer/insulin pump software), reasons for use, and self-management behavior (Self-Care Inventory-Revised, SCI-R). RESULTS: Most adolescents and parents used at least one of the 5 technologies for diabetes. Among adolescents, the most commonly used technology for diabetes was text messaging (53%), and the least commonly used was diabetes websites (25%). Most adolescents who used diabetes apps, text messaging, or pump/glucometer software did so more frequently (≥2 times per week), compared to social networking and website use (≤1 time per week). The demographic, clinical, and parent-technology use factors related to adolescents' technology use varied by technology. Adolescents who used social networking, websites, or pump/glucometer software for diabetes had better self-management behavior (SCI-R scores: beta=.18, P=.02; beta=.15, P=.046; beta=.15, P=.04, respectively), as did those who used several technologies for diabetes (beta=.23, P=.003). However, use of diabetes websites was related to poorer glycemic control (A1C: beta=.18, P=.01). CONCLUSIONS: Adolescents with T1D may be drawn to different technologies for different purposes, as individual technologies likely offer differing forms of support for diabetes self-management (eg, tracking blood glucose or aiding problem solving). Findings suggest that technologies that are especially useful for adolescents' diabetes problem solving may be particularly beneficial for their self-management. Additional research should examine relationships between the nature of technology use and adolescents' T1D self-management over time.

Technology Use for Diabetes Problem Solving in Adolescents with Type 1 Diabetes: Relationship to Glycemic Control.

BACKGROUND: This study examines technology use for problem solving in diabetes and its relationship to hemoglobin A1C (A1C). SUBJECTS AND METHODS: A sample of 112 adolescents with type 1 diabetes completed measures assessing use of technologies for diabetes problem solving, including mobile applications, social technologies, and glucose software. Hierarchical regression was performed to identify the contribution of a new nine-item Technology Use for Problem Solving in Type 1 Diabetes (TUPS) scale to A1C, considering known clinical contributors to A1C. RESULTS: Mean age for the sample was 14.5 (SD 1.7) years, mean A1C was 8.9% (SD 1.8%), 50% were female, and diabetes duration was 5.5 (SD 3.5) years. Cronbach's α reliability for TUPS was 0.78. In regression analyses, variables significantly associated with A1C were the socioeconomic status (ß = -0.26, P < 0.01), Diabetes Adolescent Problem Solving Questionnaire (ß = -0.26, P = 0.01), and TUPS (ß = 0.26, P = 0.01). Aside from the Diabetes Self-Care Inventory--Revised, each block added significantly to the model R(2). The final model R(2) was 0.22 for modeling A1C (P < 0.001). CONCLUSIONS: Results indicate a counterintuitive relationship between higher use of technologies for problem solving and higher A1C. Adolescents with poorer glycemic control may use technology in a reactive, as opposed to preventive, manner. Better understanding of the nature of technology use for self-management over time is needed to guide the development of technology-mediated problem solving tools for youth with type 1 diabetes.

Development and validation of the diabetes adolescent problem solving questionnaire.

OBJECTIVE: Problem solving is a critical diabetes self-management skill. Because of a lack of clinically feasible measures, our aim was to develop and validate a self-report self-management problem solving questionnaire for adolescents with type 1 diabetes (T1D). METHODS: A multidisciplinary team of diabetes experts generated questionnaire items that addressed diabetes self-management problem solving. Iterative feedback from parents and adolescents resulted in 27 items. Adolescents from two studies (N=156) aged 13-17 were recruited through a pediatric diabetes clinic and completed measures through an online survey. Glycemic control was measured by HbA1c recorded in the medical record. RESULTS: Empirical elimination of items using principal components analyses resulted in a 13-item unidimensional measure, the diabetes adolescent problem solving questionnaire (DAPSQ) that explained 56% of the variance. The DAPSQ demonstrated internal consistency (Cronbach's alpha=0.92) and was correlated with diabetes self-management (r=0.53, p<.001), self-efficacy (r=0.54, p<.001), and glycemic control (r=-0.24, p<.01). CONCLUSION: The DAPSQ is a brief instrument for assessment of diabetes self-management problem solving in youth with T1D and is associated with better self-management behaviors and glycemic control. PRACTICE IMPLICATIONS: The DAPSQ is a clinically feasible self-report measure that can provide valuable information regarding level of self-management problem solving and guide patient education.

Self-management problem solving for adolescents with type 1 diabetes: intervention processes associated with an Internet program.

OBJECTIVE: Describe intervention processes associated with an Internet self-management problem solving program for adolescents with type 1 diabetes, and relate participant characteristics to program use. METHODS: Forty-one adolescents with type 1 diabetes, aged 13-17, participated in an Internet intervention. RESULTS: Participants reported psychosocial self-management barriers related to social issues (45%), time pressures (22%), and emotions (25%). Most adolescents (76%) completed the two guided problem solving cycles, and most (97%) problems were appropriate and specific to diabetes. Of the 61 diabetes problems reported, 92% were mostly or completely solved. Baseline hemoglobin A1c, diabetes duration, and age were not related to online activities, however females posted more often to the forum (U=130.0, Z=2.13, p=.033). The majority of parents (87%) interacted with their child about the website. CONCLUSION: Adolescents experience psychosocial barriers to self-management that can be addressed by teaching problem solving via the Internet. PRACTICE IMPLICATIONS: An Internet self-management problem solving program with minimal external support provides a viable option for diabetes clinics to improve pediatric diabetes outcomes.

An internet-based program to improve self-management in adolescents with type 1 diabetes.

OBJECTIVE To report results from YourWay, an Internet-based self-management intervention for adolescents with type 1 diabetes. RESEARCH DESIGN AND METHODS A total of 72 adolescents with type 1 diabetes, ages 13-17 years, were randomized to a usual-care-plus-Internet support or a usual-care group. The intervention was designed to enhance problem-solving barriers to self-management. A1C was obtained from medical records, and problem-solving and self-management were obtained via adolescent report. RESULTS Group differences were not statistically significant using intent-to-treat analyses. Using as-treated analyses, adolescents in the treatment condition showed statistically significant improvement in self-management (d = 0.64; P = 0.02) and important improvements in problem-solving (d = 0.30; P = 0.23) and A1C (d = -0.28; P = 0.27). Mean A1C for the intervention group remained constant (-0.01%), while the control group increased (0.33%). CONCLUSIONS This brief trial suggests that self-management support delivered through a secure website may improve self-management and offset typical decreases in adolescent glycemic control.