Optimizing SloMo, a Digitally Supported Therapy Targeting Paranoia, for Implementation: Inclusive, Human-Centered Design Study
Bibliographic record
Abstract
BACKGROUND: Despite the promise of digital therapeutics in providing scalable interventions for psychosis, translating them from clinical trials to routine care is challenging. SloMo is an evidence-based, digitally supported cognitive behavioral therapy for psychosis comprising a web-based therapy platform and mobile app. The therapy encourages individuals to slow down for a moment, to address fast-thinking habits fueling paranoia. SloMo has received a NICE Early Value Assessment recommendation for use in the National Health Service to address evidence gaps related to its use in the real world, and an implementation study is underway. OBJECTIVE: This study aimed to optimize the SloMo software for implementation by addressing limitations of the first release, reducing technology complexity, and improving user experience, to increase equitable outcomes. METHODS: An inclusive, human-centered design methodology was used to optimize SloMo. The redesign sought to reduce the technology's complexity and improve the user experience for diverse patients and therapists. The Double Diamond framework structured the iterative redesign, integrating insights from patient and public involvement consultants, therapists, and a transdisciplinary co-design team. The Double Diamond process was facilitated through 24 transdisciplinary workshops. These were supported by the following methods: identifying implementation barriers through desk research of SloMo's evidence and qualitative interviews with experts by experience (n=2); redefining user needs; iteratively developing solutions through user testing sessions with service user consultants (n=32); and validating the minimum viable product through think-aloud testing sessions with therapist (n=10) and service user (n=11) consultants. RESULTS: Users wanted a form of cognitive behavioral therapy for psychosis that was usable, trustworthy, enjoyable, personalized, normalizing, and memorable. The redesign, therefore, included a minimalist user interface, more diverse lived experience vignette content, enhanced agency over data, greater representation of protected characteristics and their intersectionality, and intuitive navigation. Feedback from a purposively sampled patient and therapist sample validated the redesign as it was associated with a strong user experience, particularly in relation to usability and usefulness. CONCLUSIONS: The study produced a redesign of the SloMo software optimized for real-world use, whilst retaining fidelity to the therapeutic content of the previous version. Through an inclusive, human-centered approach, the optimized design of SloMo addresses barriers to adoption by reducing complexity and fostering accessibility. This study underscores the value of integrating lived experience involvement into digital therapeutics to support scalable, equitable, and sustainable mental health care solutions.
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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 | 0.033 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".