Expectations of People With Type 1 Diabetes About Voice-based Interventions to Support Diabetes Distress Management

New technologies restore people with type 1 diabetes to a certain degree of independence and control over their lives while allowing medical providers to keep an overview of their patient's general health conditions and the evolution of their treatments. Notably, solutions integrating voice-processing technology appeared to be encouraging alternatives for remote monitoring. Diabetes distress is a common condition in people living with type 1 diabetes. Diabetes distress is associated with poor metabolic control and low quality of life. Because voice analysis involves highly complex methods for processing audio features, this kind of development might also be capable of detecting subtle changes associated with psychological factors, like diabetes distress. Nevertheless, digital health incorporation into the everyday lives of potential users might also imply a big challenge for many of them. For this reason, studying the needs of the end-users of technological tools before defining any aspect of their design has become a critical step in developing this kind of technology. The main objective of Psyvoice is to identify the preferences of people living with type 1 diabetes for voice-based digital health solutions for diabetes distress detection and control. Secondary objectives are: 1. Delineating the properties that an instrument for diabetes distress management must incorporate to be considered adequate by its intended users (e.g. single voice-analysis function exclusively for diabetes distress management vs multiple functions or multiple uses). 2. Determining the attributes (e.g. frequency of use) that could make a digital health solution prone to be integrated by its end-users into their everyday lives. 3. Defining the characteristics likely to make a digital health solution acceptable. These are privacy and security concerns, barriers of use (difficulties with technology, disability) and facilitators of use (technical support, use of plain language). To achieve all of these objectives, the investigators will conduct in-depth interviews. The study will combine qualitative and quantitative methods. The investigators will invite twenty people with a T1D diagnosis or caregivers of children diagnosed with this condition to participate in semi-structured in-depth interviews and questionnaires. The questionnaires will be composed of a Socio-demographic, an e-Health Literacy (eHLQ) questionnaire, and a Diabetes Distress (PAID) scale.