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Aim: Diabetes technologies come with benefits and burdens that affect their uptake and continued use. The aim of the INSPIRE measures is to capture the psychosocial aspects of automated insulin dosing systems for youth, parents, adults and partners living with type 1 diabetes.

Methods: An extensive mixed-methods investigation consisting of interviews, focus groups, cognitive debriefing and questionnaire completion was conducted across centres in the USA and UK. To validate the measures, data from 292 youth, 159 adults, 150 parents of youth and 149 partners of individuals recruited from the Type 1 Diabetes Exchange Registry were analysed. Participants completed INSPIRE questionnaires and measures of quality of life, fear of hypoglycaemia, diabetes distress, glucose monitoring satisfaction. Exploratory factor analysis assessed factor structures. Associations between INSPIRE scores and other measures, HbA1c, and technology use assessed concurrent and discriminant validity.

Results: Four brief self-report measures were completed and successfully qualified through the FDA MDDT process. Youth, adult, parent and partner measures assess psychosocial aspects of automated insulin delivery systems and provide opportunity for consistent assessment across clinical trials globally. Measures range from 17 to 22 items, are available in several languages and are reliable (a=0.95–0.97). Use of the INSPIRE measures in clinical research will be discussed in terms of evaluation and reporting of PROs.

Conclusions: INSPIRE measures assessing the psychosocial aspects of automated insulin dosing systems for youth, adults, parents and partners have meaningful factor structures and are internally consistent. The developmentally-sensitive INSPIRE measures offer added value both within clinical trials and clinical practice.

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In the mix of diabetes technology and clinical care, there may be significant benefit to assessing and addressing patient-reported outcomes (PROs), such as depression, diabetes distress, hypoglycemic fear and attitudes toward diabetes technology. When used appropriately, PRO data can serve to guide and inform key aspects of clinical care. These data, for example, can help to determine whether or not the patient will be open to diabetes technology solutions, likely to benefit from them, and likely to stay with them over the long-term. More broadly, information from PROs can contribute to more meaningful conversations between clinicians and patients, help to identify and address obstacles to self-management (including problematic use of diabetes technology) as they arise, and serve to promote patients’ engagement in their own care. In this brief presentation, we will consider how PROs are used and misused in clinical practice, and will provide practical tips for how they may be used most effectively.