skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.
Attention:The NSF Public Access Repository (NSF-PAR) system and access will be unavailable from 7:00 AM ET to 7:30 AM ET on Friday, April 24 due to maintenance. We apologize for the inconvenience.


Title: It’s Like an Educated Guessing Game: Parents’ Strategies for Collaborative Diabetes Management with Their Children
Children with Type 1 Diabetes (T1D) face many challenges with keeping their blood glucose levels within a healthy range because they cannot manage their illness by themselves. To prevent children’s blood glucose from becoming too high or too low, parents apply different strategies to avoid risky situations. To understand how parents of children with T1D manage these risks, we conducted semi-structured interviews with children with T1D (ages 6-12) and their parents (N=41). We identified four types of strategies used by parents (i.e., educated guessing game, contingency planning, experimentation, and reaching out for help) that can be categorized according to two dimensions: 1) the cause of risk (known or unknown) and 2) the occurrence of risk (predictable or unpredictable). Based on our findings, we provide design implications for collaborative health technologies that support parents in better planning for contingencies and identifying unknown causes of risks together with their children.  more » « less
Award ID(s):
1942547
PAR ID:
10427062
Author(s) / Creator(s):
; ; ; ; ;
Date Published:
Journal Name:
ACM/SIGCHI Conference on Human Factors in Computing Systems (CHI 2023)
Page Range / eLocation ID:
1 to 15
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Mueller, Florian Floyd; Kyburz, Penny; Williamson, Julie R; Sas, Corina; Wilson, Max L; Dugas, Phoebe Toups; Shklovski, Irina (Ed.)
    Efficient Type 1 Diabetes (T1D) management necessitates comprehensive tracking of various factors that influence blood sugar levels. However, tracking health data for children with T1D poses unique challenges, as it requires the active involvement of both children and their parents. This study aims to uncover the benefits, challenges, and strategies associated with collaborative tracking for children (ages 6-12) with T1D and their parents. Over a three-week data collection probe study with 22 child-parent pairs, we found that collaborative tracking, characterized by the shared responsibility of tracking management and data provision, yielded positive outcomes for both children and their parents. Drawing from these findings, we delineate four distinct tracking approaches: child-independent, child-led, parent-led, and parent-independent. Our study offers insights for designing health technologies that empower both children and parents in learning and encourage the sharing of different perspectives through collaborative tracking. 
    more » « less
  2. Although child participation is required for successful Type 1 Diabetes (T1D) management, it is challenging because the child’s young age and immaturity make it difficult to perform self-care. Thus, parental caregivers are expected to be heavily involved in their child’s everyday illness management. Our study aims to investigate how children and parents collaborate to manage T1D and examine how the children become more independent in their self-management through the support of their parents. Through semi-structured interviews with children with T1D and their parents (N=41), our study showed that children’s knowledge of illness management and motivation for self-care were crucial for their transition towards independence. Based on these two factors, we identified four types of children’s collaboration (i.e., dependent, resistant, eager, and independent) and parents’ strategies for supporting their children’s independence. We suggest design implications for technologies to support collaborative care by improving children’s transition to independent illness management. 
    more » « less
  3. This research investigates U.S. parents’ responses to the rapidly changing, novel environment of the internet, applying evolutionary theory and interdisciplinary methodologies. Novel environments pose potential challenges to existing adaptive strategies, so this research investigates important questions about how parents and children perceive the risks of children’s entry into the virtual world and how they mitigate potential risks. The research focuses on parents of children in middle childhood (children ages 6–12), a significant period in human life history when children start building relationships outside the family. We utilize in-depth interviews (n = 26), cultural domain analysis (n = 32), surveys (n = 199), and participatory co-design (n = 34) to synergize theoretical concepts in evolutionary anthropology with the applied research focus of human–computer interaction. Cultural domain maps and interview results identify and classify perceptions of costs, benefits, and risks, including intrinsic and extrinsic sources of risk and risk tangibility. Survey results further identify platforms and risks of highest priority and confirm parental interest in new kinds of tools for managing the digital experiences of their children. Life history theory informs our approach to the development of parental control software that favors skill building and encourages parent–child discussions supporting child executive function and resilience to risks. 
    more » « less
  4. Mobile social media applications ("apps"), such as TikTok (previously Musical.ly), have recently surfaced in news media due to harmful incidents involving young children engaging with strangers through these mobile apps. To better understand children's awareness of online stranger danger and explore their visions for technologies that can help them manage related online risks (e.g., sexual solicitations and cyberbullying), we held two participatory design sessions with 12 children (ages 8-11 years old). We found that children desired varying levels of agency, depending on the severity of the risk. In most cases, they wanted help resolving the issue themselves instead of relying on their parents to do it for them. Children also believed that social media apps should take on more responsibility in promoting online safety for children. We discuss the children's desires for agency, privacy, and automated intelligent assistance and provide novel design recommendations inspired by children. 
    more » « less
  5. Abstract Continuous monitoring of blood glucose (BG) levels is a key aspect of diabetes management. Patients with Type-1 diabetes (T1D) require an effective tool to monitor these levels in order to make appropriate decisions regarding insulin administration and food intake to keep BG levels in target range. Effectively and accurately predicting future BG levels at multi-time steps ahead benefits a patient with diabetes by helping them decrease the risks of extremes in BG including hypo- and hyperglycemia. In this study, we present a novel multi-component deep learning model that predicts the BG levels in a multi-step look ahead fashion. The model is evaluated both quantitatively and qualitatively on actual blood glucose data for 97 patients. For the prediction horizon (PH) of 30 mins, the average values for root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and normalized mean squared error (NRMSE) are $$23.22 \pm 6.39$$ 23.22 ± 6.39 mg/dL, 16.77 ± 4.87 mg/dL, $$12.84 \pm 3.68$$ 12.84 ± 3.68 and $$0.08 \pm 0.01$$ 0.08 ± 0.01 respectively. When Clarke and Parkes error grid analyses were performed comparing predicted BG with actual BG, the results showed average percentage of points in Zone A of $$80.17 \pm 9.20$$ 80.17 ± 9.20 and $$84.81 \pm 6.11,$$ 84.81 ± 6.11 , respectively. We offer this tool as a mechanism to enhance the predictive capabilities of algorithms for patients with T1D. 
    more » « less