ArticleDiabetic medicine : a journal of the British Diabetic Association2025
The usability and feasibility of a self-compassion chatbot (COMPASS) for youth living with type 1 diabetes.
Article in Diabetic medicine : a journal of the British Diabetic Association, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Evidence and Future Directions for Pediatric Health Care Chatbots: Systematic Review.Journal of medical Internet research · 2026Pooled it
- Artificial Intelligence and Machine Learning in Pediatric Endocrine Tumors: Opportunities, Pitfalls, and a Roadmap for Trustworthy Clinical Translation.Biomedicines · 2026Review
- Commentary on "The effects of self-compassion in adolescents and young adults with type 1 diabetes: a pilot randomized controlled trial".Annals of pediatric endocrinology & metabolism · 2025Article
- The usability and feasibility of a self-compassion chatbot (COMPASS) for youth living with type 1 diabetes.Diabetic medicine : a journal of the British Diabetic Association · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
Abstract
aimAlthough it is well established that youth with type 1 Diabetes (T1D) experience high rates of distress, current clinical care is often under-resourced and unable to provide sufficient or timely psychological support. The current study was designed to evaluate the safety, usability and feasibility of 'COMPASS,' a self-compassion chatbot intervention.
methodsForty adolescents (aged 12-16 years) living with T1D participated in a 12-week, single-group feasibility study using a mixed methods approach. Usability was assessed via qualitative feedback and interviews, feasibility through app analytics and recruitment data, and safety by utilisation of distress risk management protocols. Psychosocial measures (including diabetes distress, strengths and resilience, self-efficacy, self-compassion, self-care behaviours and emotional well-being) were collected at baseline, 6- and 12-week follow-ups, alongside HbA1c at baseline and 12 weeks.
resultsOf 40 participants recruited, 32 (80%) completed the study. On average, participants completed 95% of modules started. Follow-up assessments showed promising reductions in diabetes distress, and improvements in resilience, self-efficacy, self-compassion and emotional well-being. Qualitative analysis highlighted strengths in engagement and relevance of information covered. Areas for improvement included increasing decision-making support to help alleviate the felt burden of diabetes.
conclusionsThe COMPASS chatbot was demonstrated to be safe, feasible and acceptable to adolescents living with T1D. Future research aims to conduct a hybrid type 1 effectiveness-implementation trial that will leverage updates in artificial intelligence to increase the flexibility and tailoring of responding.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.