RESUMEN
BACKGROUND: Our aim was to determine whether child attachment to parents, parent attachment style, and morning cortisol levels were related to diabetes outcomes measured by average glycated hemoglobin (HbA1c), HbA1c variability over 4 years and time in range (TIR) in children with type 1 diabetes (T1D). RESEARCH DESIGN AND METHODS: 101 children with T1D and one of their parents were assessed at baseline for child attachment (Child Attachment Interview; CAI) and parent attachment (Relationship Structures Questionnaire; ECR-RS). Serum samples were collected for cortisol measurements before the interviews. HbA1c levels were measured during a 4-year follow-up period at regular 3-monthly visits, and data for TIR were exported from blood glucose measuring devices. Multivariate linear regression models were constructed to identify independent predictors of glycemic outcomes. RESULTS: More girls than boys exhibited secure attachment to their mothers. The results of the regression models showed that securely attached girls (CAI) had higher average HbA1c than did insecurely attached girls (B = -0.64, p = 0.03). In boys, the more insecure the parent's attachment style, the worse the child's glycemic outcome: the higher the average Hb1Ac (B = 0.51, p = 0.005), the higher the HbA1c variability (B = 0.017, p = 0.011), and the lower the TIR (B = -8.543, p = 0.002). CONCLUSIONS: Attachment in close relationships is associated with glycemic outcomes in children with T1D, and we observed significant differences between sexes. A sex- and attachment-specific approach is recommended when treating children with less favorable glycemic outcomes. Special attention and tailored support should be offered to securely attached girls in transferring responsibility for diabetes care and at least to male children of insecurely attached parents to prevent suboptimal glycemic control. Further studies in larger samples and more daily cortisol measurements may help us better understand the links between stress response, attachment and T1D.
RESUMEN
The basis of blockchain-related data, stored in distributed ledgers, are digitally signed transactions. Data can be stored on the blockchain ledger only after a digital signing process is performed by a user with a blockchain-based digital identity. However, this process is time-consuming and not user-friendly, which is one of the reasons blockchain technology is not fully accepted. In this paper, we propose a machine learning-based method, which introduces automated signing of blockchain transactions, while including also a personalized identification of anomalous transactions. In order to evaluate the proposed method, an experiment and analysis were performed on data from the Ethereum public main network. The analysis shows promising results and paves the road for a possible future integration of such a method in dedicated digital signing software for blockchain transactions.
RESUMEN
Wearable devices have recently received considerable interest due to their great promise for a plethora of applications. Increased research efforts are oriented towards a non-invasive monitoring of human health as well as activity parameters. A wide range of wearable sensors are being developed for real-time non-invasive monitoring. This paper provides a comprehensive review of sensors used in wrist-wearable devices, methods used for the visualization of parameters measured as well as methods used for intelligent analysis of data obtained from wrist-wearable devices. In line with this, the main features of commercial wrist-wearable devices are presented. As a result of this review, a taxonomy of sensors, functionalities, and methods used in non-invasive wrist-wearable devices was assembled.
