A Novel Digital Intervention to Facilitate Diabetes Self-Management Among People with Schizophrenia and Related Disorders: Development and Acceptability Testing of SMART
Bibliographic record
Abstract
Urska Arnautovska,1– 3 Gabrielle Ritchie,1– 3 Rebecca Soole,1,3 Anish Menon,1,4 Nicole Korman,2 Alyssa Milton,5,6 Marlien Varnfield,7 Jaimon T Kelly,4 Pieter M Jansen,8 Andrea Baker,3 Derek Ireland,7 Anthony W Russell,4,9,10 Justin Chapman,2 Kathleen Mulligan,11,12 Shashivadan P Hirani,11 Kathryn Jemimah Vitangcol,1 Gemma McKeon,3,13,14 Dan Siskind1– 3 1Faculty of Health, Medicine, and Behavioural Sciences, University of Queensland, Brisbane, Queensland, Australia; 2Metro South Addiction and Mental Health Services, Queensland Health, Brisbane, Queensland, Australia; 3Queensland Centre for Mental Health Research, Brisbane, Queensland, Australia; 4Centre for Online Research, Faculty of Medicine, University of Queensland, Brisbane, Australia; 5Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia; 6ARC Centre of Excellence for Children and Families Over the Life Course, Indooroopilly, Queensland, Australia; 7The Australian eHealth Research Centre, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Brisbane, Queensland, Australia; 8The Princess Alexandra Hospital, Department of Diabetes and Endocrinology, Brisbane, Queensland, Australia; 9The Alfred Hospital, Melbourne, Victoria, Australia; 10School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia; 11School of Health and Medical Sciences, Department of Health Services Research and Management, City St George’s University of London, London, UK; 12East London NHS Foundation Trust, London, UK; 13University of Queensland, Child Health Research Centre, Brisbane, Queensland, Australia; 14West Moreton Health Psychology, Ipswich, Queensland, AustraliaCorrespondence: Urska Arnautovska, Queensland Centre for Mental Health Research, Locked Bag 500, Archerfield, Brisbane, QLD 4108, Australia, Email u.arnautovska@uq.edu.auIntroduction: Compared with the general population, people with schizophrenia and schizophrenia-related disorders (SSD) have a higher prevalence of type 2 diabetes (T2D) and T2D risk factors such as poor diet and sedentary lifestyle. Antipsychotic drugs significantly contribute to this risk through metabolic adverse effects, including weight gain and insulin resistance. Prevention and self-management of T2D is challenging in this population due to inherent motivational and cognitive challenges associated with schizophrenia. The objective of this study was to describe the co-design and test the feasibility, acceptability, and usability of a novel digital health intervention, Schizophrenia and diabetes Mobile-Assisted Remote Trainer (SMART), for prevention and self-management of T2D in people with SSD.Methods: SMART was developed through an iterative process including review of relevant literature (eg, disease-specific guidelines), stakeholder involvement, and user testing. A pre-post mixed-methods design was used to assess the acceptability and feasibility of SMART over 4 weeks among five outpatients with schizophrenia/schizoaffective disorder and pre-diabetes/T2D.Results: The co-design process resulted in a digital intervention, which consisted of personalised, interactive text messages, providing psychoeducation and strengthening motivation for self-care behaviours that promote effective diabetes self-management (ie, nutrition, physical activity, weight management, and stress coping). The pilot study demonstrated good acceptability of SMART (response rates 75– 95%). Trends towards improved clinical outcomes were observed in well-being, depression, anxiety, and mental health recovery. Barriers to usability included lack of mobile/internet data, precluding the ability to reply to text messages, and a preference for more hyperlinks and additional interactive features.Conclusion: The comprehensive co-design process resulted in the development of a novel digital intervention for prevention and self-management of T2D tailored to unique needs and preferences of people with SSD. The pilot study findings indicate that SMART is acceptable and potentially usable for this population. Results will inform further adaptation and a future feasibility study to examine preliminary effectiveness of SMART.Keywords: eHealth, mHealth, psychosis, SMS, mobile phone, metabolic syndrome, mental health interventions, diabetes, self-management
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".